{"ts": "2026-07-17T15:35:16.747140+00:00", "type": "system_prompt", "text": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}
{"ts": "2026-07-17T15:35:16.748645+00:00", "type": "user", "text": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}
{"ts": "2026-07-17T15:35:43.377742+00:00", "type": "llm_call", "call_index": 1, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:16.748645+00:00", "finished_at": "2026-07-17T15:35:43.377742+00:00", "duration_s": 26.625, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:16.748645+00:00", "finished_at": "2026-07-17T15:35:43.377742+00:00", "duration_s": 26.625, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "21826", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend 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red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}],\"reasoning_details\":[{\"type\":\"reasoning.summary\",\"summary\":\"**Planning 3D Modeling in Python**\\n\\nI need to build a project in Python, making sure everything renders correctly. I’ll create mesh primitives, approximating a teapot using UV spheres, cones, and toruses, and a bunny with ellipsoids for the ears. For Suzanne, I’ll use the actual monkey model. I’ll include sphere checker material nodes and possibly use cloth planes or cubes for drapes, a table, backdrop, and red-green panels. I also need warm area lighting and a camera set up. It’s time to execute a large script!\",\"format\":\"openai-responses-v1\",\"index\":0},{\"type\":\"reasoning.encrypted\",\"data\":\"gAAAAABqWkvDH24TUtstM9T_l2p9-uFFOeqV03srVnfo5kG93P_GfRXS9JPowG__y6bu65mwrT_uIkC9vwF3YBginq_-hbmQtvZ5tywAjwvXhlZ8i1ffAIFruHdoLgGBg4OlPHkXXJskjbHYtKJDR-zg6PcplOCnychqBV-0CrU534W5PFbFI28R0a7B0Wxx6bCYbq_RL7gcEx4Mp1eeIusCtUdAbbQkVtf7Q8jqFx89QmI7cviv30WEusCum3SUDmUZ0J9zOU1jLqJcTM_-c8un_kT4ljoj2uVew7NuPDoSeySdHEdqh7wFda4sJmhfW9-P7DopzRSGCRXlT2HTmkKIvi-ggJ-M1bJs4gnx8f2qt7r3ewlyevTGHixLFze5Izt1XFu_NuJpC0eDa8SV3e3ncyB5vtm5_rPpQiY6IjXERs0rBGurYByYuhVTDmWIneJCBr3QD41XNe6WBicMnaize7Xb2uk2OiKsF-8heI7Zkim3T_5m4AwG1iDIOP_mhDp5FZsGjyzzSbrzAO9oCk8c8pkVCzsn4FM0d1rTZR7EuQq6JLq9T27k9ftN6uVvt-PnlQnMickFdQLOL4LkbMpEvj1qt4nwzhckIFLeJLswvayaZKzEASm_IttanDzMUMM3BtfcE-6-t8cVBCUQN--GThVF4mnCg3RLkhRUpdd4ISfKpXp3YxLKIz17yHacT-eyud5XSv3VDvcBgivI4tMj_4_nd44cFW-bM4zI8iFCV84wcx_eqJqEDkpsVnhhbeJZRA7Nu5zK6PP4YQ8ADgKuk_jXAg5kMFXCr5TDFmmGhjIKEuq_Ong6_lnRiW4953GSq-RKoVf65hBpyjUnftta8F1hdpfhrw6n9isZ5Zwjcl0onklMgC0kJw616Nr_smorV-gpFQHqtj0KgXZX92-cDncyQNql77eZOm_MW9DNbhQFnUpoKjMgrOXyDlH0zPSpQWT9xCNoC5V8ZyPB7A1xRhh9z9DkalPID2m3VZRn6WwUSi9RnvKbisGRRekOj9E0mRi1vi5xKxh9e-AzdA2uFZ6rTpV2kvikbnMLZB-SGET_bOQjMZ9IeRiawQgFdfOJk3qMGlzLAZ1f0CQ4RNh3tWq3-z0TI8M5vR6ogU7pppewOQncYCqffY8KZJ0rsUQbud4n62m3uP0Tsn2-Qo9GmCZROK7VfUX8sw2JS_OWt5rrluAeqA7c1sPaIIMBHm1u7ZnpQp8gwHWpIoKllvGPT0Q69dslIzqVtZPZyCM1N5tNEOxY_uuqGcWGcK9VtK8CKyKzb-Q1FqG4DUgL9PFSDTxt5dUFYTRx6gz8pQ6JAHhWzSkwn4gVJw7dWxuMOcnA8XH9ulvZryqYPz9LRMJ_I0NcWye2WA==\",\"format\":\"openai-responses-v1\",\"id\":\"rs_04aca26bc2eb5f66016a5a4bc161ec8193bd8dbd034a8cd40e\",\"index\":1}]}}],\"usage\":{\"prompt_tokens\":3971,\"completion_tokens\":2636,\"total_tokens\":6607,\"cost\":0.020779,\"is_byok\":false,\"prompt_tokens_details\":{\"cached_tokens\":0,\"cache_write_tokens\":3968,\"audio_tokens\":0,\"video_tokens\":0},\"cost_details\":{\"upstream_inference_cost\":0.020779,\"upstream_inference_prompt_cost\":0.004963,\"upstream_inference_completions_cost\":0.015816},\"completion_tokens_details\":{\"reasoning_tokens\":73,\"image_tokens\":0,\"audio_tokens\":0}}}", "body_base64": "
         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         

         
{"id":"gen-1784302528-dIDoVFvYYbAmGxLNhqmE","object":"chat.completion","created":1784302528,"model":"openai/gpt-5.6-luna","provider":"OpenAI","system_fingerprint":null,"service_tier":"default","choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"completed","message":{"role":"assistant","content":null,"refusal":null,"reasoning":"**Planning 3D Modeling in Python**\n\nI need to build a project in Python, making sure everything renders correctly. I’ll create mesh primitives, approximating a teapot using UV spheres, cones, and toruses, and a bunny with ellipsoids for the ears. For Suzanne, I’ll use the actual monkey model. I’ll include sphere checker material nodes and possibly use cloth planes or cubes for drapes, a table, backdrop, and red-green panels. I also need warm area lighting and a camera set up. It’s time to execute a large script!","tool_calls":[{"type":"function","index":0,"id":"call_pcdGD8DIsErWadSESCdy7w1o","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}],"reasoning_details":[{"type":"reasoning.summary","summary":"**Planning 3D Modeling in Python**\n\nI need to build a project in Python, making sure everything renders correctly. I’ll create mesh primitives, approximating a teapot using UV spheres, cones, and toruses, and a bunny with ellipsoids for the ears. For Suzanne, I’ll use the actual monkey model. I’ll include sphere checker material nodes and possibly use cloth planes or cubes for drapes, a table, backdrop, and red-green panels. I also need warm area lighting and a camera set up. It’s time to execute a large script!","format":"openai-responses-v1","index":0},{"type":"reasoning.encrypted","data":"gAAAAABqWkvDH24TUtstM9T_l2p9-uFFOeqV03srVnfo5kG93P_GfRXS9JPowG__y6bu65mwrT_uIkC9vwF3YBginq_-hbmQtvZ5tywAjwvXhlZ8i1ffAIFruHdoLgGBg4OlPHkXXJskjbHYtKJDR-zg6PcplOCnychqBV-0CrU534W5PFbFI28R0a7B0Wxx6bCYbq_RL7gcEx4Mp1eeIusCtUdAbbQkVtf7Q8jqFx89QmI7cviv30WEusCum3SUDmUZ0J9zOU1jLqJcTM_-c8un_kT4ljoj2uVew7NuPDoSeySdHEdqh7wFda4sJmhfW9-P7DopzRSGCRXlT2HTmkKIvi-ggJ-M1bJs4gnx8f2qt7r3ewlyevTGHixLFze5Izt1XFu_NuJpC0eDa8SV3e3ncyB5vtm5_rPpQiY6IjXERs0rBGurYByYuhVTDmWIneJCBr3QD41XNe6WBicMnaize7Xb2uk2OiKsF-8heI7Zkim3T_5m4AwG1iDIOP_mhDp5FZsGjyzzSbrzAO9oCk8c8pkVCzsn4FM0d1rTZR7EuQq6JLq9T27k9ftN6uVvt-PnlQnMickFdQLOL4LkbMpEvj1qt4nwzhckIFLeJLswvayaZKzEASm_IttanDzMUMM3BtfcE-6-t8cVBCUQN--GThVF4mnCg3RLkhRUpdd4ISfKpXp3YxLKIz17yHacT-eyud5XSv3VDvcBgivI4tMj_4_nd44cFW-bM4zI8iFCV84wcx_eqJqEDkpsVnhhbeJZRA7Nu5zK6PP4YQ8ADgKuk_jXAg5kMFXCr5TDFmmGhjIKEuq_Ong6_lnRiW4953GSq-RKoVf65hBpyjUnftta8F1hdpfhrw6n9isZ5Zwjcl0onklMgC0kJw616Nr_smorV-gpFQHqtj0KgXZX92-cDncyQNql77eZOm_MW9DNbhQFnUpoKjMgrOXyDlH0zPSpQWT9xCNoC5V8ZyPB7A1xRhh9z9DkalPID2m3VZRn6WwUSi9RnvKbisGRRekOj9E0mRi1vi5xKxh9e-AzdA2uFZ6rTpV2kvikbnMLZB-SGET_bOQjMZ9IeRiawQgFdfOJk3qMGlzLAZ1f0CQ4RNh3tWq3-z0TI8M5vR6ogU7pppewOQncYCqffY8KZJ0rsUQbud4n62m3uP0Tsn2-Qo9GmCZROK7VfUX8sw2JS_OWt5rrluAeqA7c1sPaIIMBHm1u7ZnpQp8gwHWpIoKllvGPT0Q69dslIzqVtZPZyCM1N5tNEOxY_uuqGcWGcK9VtK8CKyKzb-Q1FqG4DUgL9PFSDTxt5dUFYTRx6gz8pQ6JAHhWzSkwn4gVJw7dWxuMOcnA8XH9ulvZryqYPz9LRMJ_I0NcWye2WA==","format":"openai-responses-v1","id":"rs_04aca26bc2eb5f66016a5a4bc161ec8193bd8dbd034a8cd40e","index":1}]}}],"usage":{"prompt_tokens":3971,"completion_tokens":2636,"total_tokens":6607,"cost":0.020779,"is_byok":false,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":3968,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.020779,"upstream_inference_prompt_cost":0.004963,"upstream_inference_completions_cost":0.015816},"completion_tokens_details":{"reasoning_tokens":73,"image_tokens":0,"audio_tokens":0}}}"}, "error": null}], "response": {"text": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name,color,metal=0,rough=.4):\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\n# helpers\ndef cube(n,loc,scale,ma,bev=0):\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\n o.data.materials.append(ma);return o\ndef uv(n,loc,scale,ma):\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\ndef cyl(n,loc,r,dep,ma,verts=32):\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\n# stage\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\n# Cornell fabric panels facing across\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\n# cloth swags on table\nfor i in range(7):\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\n# TEAPOT body at hero\ncx,cy=.15,.35\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\n# shoulder/lid\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\n# spout angled right/front using cone between points\ndef cone_between(n,a,b,r1,r2,ma):\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\n# handle torus behind\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\n# bunny left: body, haunch, head, ears\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\n# Suzanne bust right rear\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\n# checker sphere foreground right nodes\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\n# candlestick left front\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\n# orange fruit\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\n# open book right\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\n# ground rug neutral\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\n# camera\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,1.35))\n# light single warm shaft\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\n# world\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\n# settings\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\n# camera DOF\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}", "parse_error": null}], "tokens_in": 3971, "tokens_out": 2636, "cost_usd": 0.020779, "cached_tokens": 0}, "error": null}
{"ts": "2026-07-17T15:35:43.379246+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name,color,metal=0,rough=.4):\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\n# helpers\ndef cube(n,loc,scale,ma,bev=0):\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\n o.data.materials.append(ma);return o\ndef uv(n,loc,scale,ma):\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\ndef cyl(n,loc,r,dep,ma,verts=32):\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\n# stage\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\n# Cornell fabric panels facing across\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\n# cloth swags on table\nfor i in range(7):\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\n# TEAPOT body at hero\ncx,cy=.15,.35\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\n# shoulder/lid\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\n# spout angled right/front using cone between points\ndef cone_between(n,a,b,r1,r2,ma):\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\n# handle torus behind\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\n# bunny left: body, haunch, head, ears\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\n# Suzanne bust right rear\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\n# checker sphere foreground right nodes\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\n# candlestick left front\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\n# orange fruit\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\n# open book right\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\n# ground rug neutral\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\n# camera\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,1.35))\n# light single warm shaft\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\n# world\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\n# settings\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\n# camera DOF\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}", "parse_error": null}]}
{"ts": "2026-07-17T15:35:43.379246+00:00", "type": "tool_call", "id": "call_pcdGD8DIsErWadSESCdy7w1o", "name": "execute_blender_code", "arguments": {"code": "import bpy, math\nfrom mathutils import Vector\n# clear\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\n    pass\n# materials\ndef mat(name,color,metal=0,rough=.4):\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\n# helpers\ndef cube(n,loc,scale,ma,bev=0):\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\n o.data.materials.append(ma);return o\ndef uv(n,loc,scale,ma):\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\ndef cyl(n,loc,r,dep,ma,verts=32):\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\n# stage\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\n# Cornell fabric panels facing across\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\n# cloth swags on table\nfor i in range(7):\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\n# TEAPOT body at hero\ncx,cy=.15,.35\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\n# shoulder/lid\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\n# spout angled right/front using cone between points\ndef cone_between(n,a,b,r1,r2,ma):\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\n# handle torus behind\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\n# bunny left: body, haunch, head, ears\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\n# Suzanne bust right rear\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\n# checker sphere foreground right nodes\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\n# candlestick left front\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\n# orange fruit\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\n# open book right\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\n# ground rug neutral\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\n# camera\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\ntrack(cam,(0,0,1.35))\n# light single warm shaft\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\n# world\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\n# settings\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\n# camera DOF\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}"}, "raw_arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}
{"ts": "2026-07-17T15:35:44.768979+00:00", "type": "tool_result", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "name": "execute_blender_code", "text": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-17T15:35:47.622154+00:00", "type": "llm_call", "call_index": 2, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:44.770340+00:00", "finished_at": "2026-07-17T15:35:47.622154+00:00", "duration_s": 2.859000000025844, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:44.770340+00:00", "finished_at": "2026-07-17T15:35:47.622154+00:00", "duration_s": 2.859000000025844, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "30091", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name,color,metal=0,rough=.4):\\\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 69, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_pcdGD8DIsErWadSESCdy7w1o","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_pcdGD8DIsErWadSESCdy7w1o","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ","parameters":{"properties":{"identifier":{"title":"Identifier","type":"string"}},"required":["identifier"],"title":"get_python_api_docsArguments","type":"object"}}},{"type":"function","function":{"name":"get_screenshot_of_area_as_image","description":"\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ","parameters":{"properties":{"area_ui_type":{"enum":["VIEW_3D","IMAGE_EDITOR","UV","ShaderNodeTree","CompositorNodeTree","GeometryNodeTree","TextureNodeTree","SEQUENCE_EDITOR","CLIP_EDITOR","DOPESHEET_EDITOR","GRAPH_EDITOR","NLA_EDITOR","TEXT_EDITOR","CONSOLE","INFO","TOPBAR","STATUSBAR","OUTLINER","PROPERTIES","FILE_BROWSER","SPREADSHEET","PREFERENCES"],"title":"Area Ui Type","type":"string"},"size_limit_in_bytes":{"default":0,"title":"Size Limit In 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{"ts": "2026-07-17T15:35:47.623160+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_kzbbnenHNiRLt0EPETYzrO3B", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene\nsc.render.engine='BLENDER_EEVEE'\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\nif cam:\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}", "parse_error": null}]}
{"ts": "2026-07-17T15:35:47.623160+00:00", "type": "tool_call", "id": "call_kzbbnenHNiRLt0EPETYzrO3B", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene\nsc.render.engine='BLENDER_EEVEE'\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\nif cam:\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}
{"ts": "2026-07-17T15:35:47.733108+00:00", "type": "tool_result", "tool_call_id": "call_kzbbnenHNiRLt0EPETYzrO3B", "name": "execute_blender_code", "text": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-17T15:35:50.271203+00:00", "type": "llm_call", "call_index": 3, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:47.733108+00:00", "finished_at": "2026-07-17T15:35:50.271203+00:00", "duration_s": 2.5309999999881256, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_kzbbnenHNiRLt0EPETYzrO3B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_kzbbnenHNiRLt0EPETYzrO3B", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:47.733108+00:00", "finished_at": "2026-07-17T15:35:50.271203+00:00", "duration_s": 2.5309999999881256, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "31976", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name,color,metal=0,rough=.4):\\\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 69, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\\\nif cam:\\\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 11, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_pcdGD8DIsErWadSESCdy7w1o","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_pcdGD8DIsErWadSESCdy7w1o","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_kzbbnenHNiRLt0EPETYzrO3B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_kzbbnenHNiRLt0EPETYzrO3B","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return linked-library info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_of_linked_libraries_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info","description":"\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_path_infoArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_path_info_for_cli","description":"\n        Return path info by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_path_info_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess","description":"\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_usage_guessArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_usage_guess_for_cli","description":"\n        Guess use-cases by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_usage_guess_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_object_detail_summary","description":"\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ","parameters":{"properties":{"name":{"title":"Name","type":"string"}},"required":["name"],"title":"get_object_detail_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_objects_summary","description":"\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ","parameters":{"properties":{},"title":"get_objects_summaryArguments","type":"object"}}},{"type":"function","function":{"name":"get_python_api_docs","description":"\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. 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{"ts": "2026-07-17T15:35:50.272209+00:00", "type": "assistant", "text": null, "tool_calls": [{"id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}", "parse_error": null}]}
{"ts": "2026-07-17T15:35:50.272209+00:00", "type": "tool_call", "id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "name": "execute_blender_code", "arguments": {"code": "import bpy\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\ntry: sc.view_settings.look='AgX - Medium High Contrast'\nexcept: pass\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}"}, "raw_arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}
{"ts": "2026-07-17T15:35:52.002901+00:00", "type": "tool_result", "tool_call_id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "name": "execute_blender_code", "text": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}", "is_error": false, "has_image": false, "image_b64": null}
{"ts": "2026-07-17T15:35:54.308265+00:00", "type": "llm_call", "call_index": 4, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:52.002901+00:00", "finished_at": "2026-07-17T15:35:54.308265+00:00", "duration_s": 2.312999999994645, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_kzbbnenHNiRLt0EPETYzrO3B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_kzbbnenHNiRLt0EPETYzrO3B", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:52.002901+00:00", "finished_at": "2026-07-17T15:35:54.308265+00:00", "duration_s": 2.312999999994645, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "33294", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name,color,metal=0,rough=.4):\\\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 69, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\\\nif cam:\\\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 11, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 40,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_graphics_still_life.blend\\\",\\n    \\\"render\\\": \\\"//stilllife.png\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_graphics_still_life.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_pcdGD8DIsErWadSESCdy7w1o","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_pcdGD8DIsErWadSESCdy7w1o","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_kzbbnenHNiRLt0EPETYzrO3B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_kzbbnenHNiRLt0EPETYzrO3B","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_YdcfrWdF6ajaYopKxbyZMEKV","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_YdcfrWdF6ajaYopKxbyZMEKV","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend 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"}
{"ts": "2026-07-17T15:35:54.637576+00:00", "type": "image", "tool": "get_screenshot_of_window_as_image"}
{"ts": "2026-07-17T15:35:57.688339+00:00", "type": "llm_call", "call_index": 5, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:54.637576+00:00", "finished_at": "2026-07-17T15:35:57.688339+00:00", "duration_s": 3.047000000020489, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_kzbbnenHNiRLt0EPETYzrO3B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_kzbbnenHNiRLt0EPETYzrO3B", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hAGSOWw9upfhn9ZHzUTM9aO6", "type": "function", "function": {"name": "get_screenshot_of_window_as_image", "arguments": "{\"size_limit_in_bytes\":200000}"}}]}, {"role": "tool", "tool_call_id": "call_hAGSOWw9upfhn9ZHzUTM9aO6", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}], 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{"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:54.637576+00:00", "finished_at": "2026-07-17T15:35:57.687338+00:00", "duration_s": 3.047000000020489, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "212463", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name,color,metal=0,rough=.4):\\\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 69, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\\\nif cam:\\\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 11, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 40,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_graphics_still_life.blend\\\",\\n    \\\"render\\\": \\\"//stilllife.png\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_graphics_still_life.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hAGSOWw9upfhn9ZHzUTM9aO6\",\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"arguments\":\"{\\\"size_limit_in_bytes\\\":200000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hAGSOWw9upfhn9ZHzUTM9aO6\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_pcdGD8DIsErWadSESCdy7w1o","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_pcdGD8DIsErWadSESCdy7w1o","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_kzbbnenHNiRLt0EPETYzrO3B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_kzbbnenHNiRLt0EPETYzrO3B","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_YdcfrWdF6ajaYopKxbyZMEKV","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_YdcfrWdF6ajaYopKxbyZMEKV","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hAGSOWw9upfhn9ZHzUTM9aO6","type":"function","function":{"name":"get_screenshot_of_window_as_image","arguments":"{\"size_limit_in_bytes\":200000}"}}]},{"role":"tool","tool_call_id":"call_hAGSOWw9upfhn9ZHzUTM9aO6","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_missing_files_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries","description":"\n        Return a tree of directly and indirectly linked library files.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_of_linked_librariesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_of_linked_libraries_for_cli","description":"\n        Return 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{"ts": "2026-07-17T15:35:59.130856+00:00", "type": "llm_call", "call_index": 6, "provider_type": "blenderbench.providers.openrouter.OpenRouterProvider", "started_at": "2026-07-17T15:35:57.749184+00:00", "finished_at": "2026-07-17T15:35:59.130856+00:00", "duration_s": 1.375, "request": {"messages": [{"role": "system", "content": "You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."}, {"role": "user", "content": "## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_pcdGD8DIsErWadSESCdy7w1o", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_pcdGD8DIsErWadSESCdy7w1o", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_kzbbnenHNiRLt0EPETYzrO3B", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]}, {"role": "tool", "tool_call_id": "call_kzbbnenHNiRLt0EPETYzrO3B", "content": "{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]}, {"role": "tool", "tool_call_id": "call_YdcfrWdF6ajaYopKxbyZMEKV", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_hAGSOWw9upfhn9ZHzUTM9aO6", "type": "function", "function": {"name": "get_screenshot_of_window_as_image", "arguments": "{\"size_limit_in_bytes\":200000}"}}]}, {"role": "tool", "tool_call_id": "call_hAGSOWw9upfhn9ZHzUTM9aO6", "content": ""}, {"role": "user", "content": [{"type": "image_url", "image_url": {"url": 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"}}]}, {"role": "assistant", "content": null, "tool_calls": [{"id": "call_CUnbLXuMmDt1iUcfXfm9Slbe", "type": "function", "function": {"name": "execute_blender_code", "arguments": "{\"code\":\"import bpy\\n# switch current area to image editor for validation\\nif bpy.context.area: bpy.context.area.type='IMAGE_EDITOR'; bpy.context.area.spaces.active.image=bpy.data.images.get('Render Result')\\nresult={'rendered':bpy.data.images.get('Render Result') is not None}\"}"}}]}, {"role": "tool", "tool_call_id": "call_CUnbLXuMmDt1iUcfXfm9Slbe", "content": "{\n  \"status\": \"ok\",\n  \"result\": {\n    \"rendered\": true\n  }\n}"}], "tools": [{"type": "function", "function": {"name": "execute_blender_code", "description": "\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ", "parameters": {"properties": {"code": {"title": "Code", "type": "string"}}, "required": ["code"], "title": "execute_blender_codeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "execute_blender_code_for_cli", "description": "\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}, "code": {"title": "Code", "type": "string"}}, "required": ["blend_file", "code"], "title": "execute_blender_code_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks", "description": "\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_datablocksArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_datablocks_for_cli", "description": "\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_datablocks_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files", "description": "\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_missing_filesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_missing_files_for_cli", "description": "\n        Report missing file references by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_missing_files_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries", "description": "\n        Return a tree of directly and indirectly linked library files.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_of_linked_librariesArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_of_linked_libraries_for_cli", "description": "\n        Return linked-library info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_of_linked_libraries_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info", "description": "\n        Simple/fast access to the blend file's path, save status, age, and backups.\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_path_infoArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_path_info_for_cli", "description": "\n        Return path info by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_path_info_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess", "description": "\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\n        ", "parameters": {"properties": {}, "title": "get_blendfile_summary_usage_guessArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_blendfile_summary_usage_guess_for_cli", "description": "\n        Guess use-cases by opening *blend_file* in background Blender.\n        ", "parameters": {"properties": {"blend_file": {"title": "Blend File", "type": "string"}}, "required": ["blend_file"], "title": "get_blendfile_summary_usage_guess_for_cliArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_object_detail_summary", "description": "\n        Return a structured summary of the object identified by *name*.\n\n        Includes type, transforms, parent, children, modifiers, constraints,\n        materials, visibility, data-block name, and collections.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "get_object_detail_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_objects_summary", "description": "\n        Return the scene's collection hierarchy and their objects.\n\n        Each collection lists its objects (name, type, parent, data name,\n        selection, visibility) and nested child collections.\n        ", "parameters": {"properties": {}, "title": "get_objects_summaryArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_python_api_docs", "description": "\n        Return the Blender Python API docs for *identifier*, or list\n        modules matching a trailing-``*`` discovery pattern.\n\n        *identifier* should be a fully-qualified Python name (e.g.\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\n        The trailing-``*`` forms are supported as discovery entry-points:\n\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\n          ``mathutils``, ``gpu``, ...).\n        - ``X.*`` enumerates the direct-child identifiers under the\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\n\n        Both return a ``namespace`` response even when ``X.rst`` would\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\n        force the child listing.\n\n        The response always carries ``kind``, ``found``, and ``identifier``.\n        The remaining keys depend on ``kind``:\n\n        - ``\"exact\"`` (``found=True``): ``<identifier>.rst`` was read.\n          Extra keys: ``content`` (RST text), ``examples``. When the\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\n          summary of the file's top-level definitions (prefixed by a\n          header noting the truncation) and ``examples`` is empty -\n          re-query individual members for their rendered blocks.\n        - ``\"namespace\"`` (``found=True``):\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\n          Extra key: ``submodules`` (list of child identifiers).\n        - ``\"definition\"`` (``found=True``):\n          *identifier* is defined inside a parent RST\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\n          Extra keys: ``content`` (rendered block), ``examples``.\n        - ``\"partial\"`` (``found=False``):\n          the parent RST was located but the trailing component isn't defined in it.\n          Extra keys:\n          - ``parent`` the identifier whose RST was loaded.\n          - ``available`` top-level definitions in that RST.\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\n            filtered to those whose last component contains every character of the missing tail.\n\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\n        - ``\"suggestions\"`` (``found=False``):\n          no direct match, but *identifier* appears as a component of other files.\n          Extra key: ``suggestions`` (list of full identifiers).\n        - ``\"missing\"`` (``found=False``): nothing matched.\n\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\n        is a list of ``{path, content}`` entries referenced from this documentation.\n        ", "parameters": {"properties": {"identifier": {"title": "Identifier", "type": "string"}}, "required": ["identifier"], "title": "get_python_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_area_as_image", "description": "\n        Take a screenshot of a single Blender area and return it as a PNG image.\n\n        *area_ui_type* matches the area's ``ui_type``.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"area_ui_type": {"enum": ["VIEW_3D", "IMAGE_EDITOR", "UV", "ShaderNodeTree", "CompositorNodeTree", "GeometryNodeTree", "TextureNodeTree", "SEQUENCE_EDITOR", "CLIP_EDITOR", "DOPESHEET_EDITOR", "GRAPH_EDITOR", "NLA_EDITOR", "TEXT_EDITOR", "CONSOLE", "INFO", "TOPBAR", "STATUSBAR", "OUTLINER", "PROPERTIES", "FILE_BROWSER", "SPREADSHEET", "PREFERENCES"], "title": "Area Ui Type", "type": "string"}, "size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "required": ["area_ui_type"], "title": "get_screenshot_of_area_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_image", "description": "\n        Take a screenshot of the entire Blender window and return it as a PNG image.\n\n        *size_limit_in_bytes* caps the image size in bytes.\n        Zero (the default) uses the MCP message size limit.\n        ", "parameters": {"properties": {"size_limit_in_bytes": {"default": 0, "title": "Size Limit In Bytes", "type": "integer"}}, "title": "get_screenshot_of_window_as_imageArguments", "type": "object"}}}, {"type": "function", "function": {"name": "get_screenshot_of_window_as_json", "description": "\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\n        ", "parameters": {"properties": {}, "title": "get_screenshot_of_window_as_jsonArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_name", "description": "\n        Switch the active workspace tab to *name*.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}}, "required": ["name"], "title": "jump_to_tab_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_tab_by_space_type", "description": "\n        Switch to a workspace whose main area matches *space_type*.\n\n        If *allow_edits* is True and no matching workspace exists, a new one\n        is created by duplicating the current workspace.\n        ", "parameters": {"properties": {"space_type": {"title": "Space Type", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["space_type"], "title": "jump_to_tab_by_space_typeArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_by_name", "description": "\n        Move the 3D viewport to focus on an object by *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "jump_to_view3d_object_data_by_name", "description": "\n        Move the 3D viewport to the object whose data block matches *name*.\n\n        If *allow_edits* is True the object may be un-hidden and its\n        collections enabled to make it visible.\n        ", "parameters": {"properties": {"name": {"title": "Name", "type": "string"}, "allow_edits": {"default": false, "title": "Allow Edits", "type": "boolean"}}, "required": ["name"], "title": "jump_to_view3d_object_data_by_nameArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_thumbnail_to_path", "description": "\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_thumbnail_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "render_viewport_to_path", "description": "\n        Render the current scene to *output_path* using current render settings.\n        ", "parameters": {"properties": {"output_path": {"title": "Output Path", "type": "string"}}, "required": ["output_path"], "title": "render_viewport_to_pathArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_api_docs", "description": "\nFull-text search over the bundled Blender Python API reference.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_api_docsArguments", "type": "object"}}}, {"type": "function", "function": {"name": "search_manual_docs", "description": "\nFull-text search over the bundled Blender user manual.\n\nReturns a ranked list of hits. Each hit has:\n\n- ``path``: file path relative to the bundled docs.\n- ``text``: the matching paragraph plus ``context``\n  paragraphs on either side.\n- ``breadcrumb``: the section path containing the hit\n  (``Section > Sub-section > ...``).\n- ``index``: the hit's position in the result list.\n- ``score``: a relevance score; higher is better.\n\nThe query is tokenised on whitespace and matched\ncase-insensitively. Every token must appear somewhere in\nthe paragraph body, the file path, or an enclosing section\ntitle - in any order. Common English stop-words (``the``,\n``a``, ``how``, ``to``, ...) are dropped, so natural\nphrasings like ``\"how to bake\"`` work as expected. Regular\nexpressions are not supported.\n\nUse ``context`` to pull more surrounding paragraphs into\neach hit (symmetric, default 0). Use ``index`` with the\nposition of a previous hit (same query) to get that hit\nalone with its text widened to its enclosing section.\n\nRead-only; consults bundled RST files only.\n", "parameters": {"properties": {"query": {"title": "Query", "type": "string"}, "max_results": {"default": 20, "title": "Max Results", "type": "integer"}, "context": {"default": 0, "title": "Context", "type": "integer"}, "index": {"anyOf": [{"type": "integer"}, {"type": "null"}], "default": null, "title": "Index"}}, "required": ["query"], "title": "search_manual_docsArguments", "type": "object"}}}]}, "provider_attempts": [{"provider": "openrouter", "attempt": 1, "started_at": "2026-07-17T15:35:57.749184+00:00", "finished_at": "2026-07-17T15:35:59.129856+00:00", "duration_s": 1.375, "request": {"method": "POST", "url": "https://openrouter.ai/api/v1/chat/completions", "headers": {"host": "openrouter.ai", "accept": "*/*", "accept-encoding": "gzip, deflate", "connection": "keep-alive", "user-agent": "python-httpx/0.28.1", "authorization": "[REDACTED]", "content-length": "213064", "content-type": "application/json"}, "body": "{\"model\":\"openai/gpt-5.6-luna\",\"messages\":[{\"role\":\"system\",\"content\":\"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\\n\\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\\n\\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\\n\\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\\n\\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\\n\\n## Harness Instructions\\n\\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\\n\\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls.\"},{\"role\":\"user\",\"content\":\"## Task\\n\\nComplete the following Blender task:\\n\\n<task>\\n\\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\\n\\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \\\"portrait\\\" gazing out of the arrangement.\\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\\n\\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\\n\\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\\n\\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\\n\\n</task>\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy, math\\\\nfrom mathutils import Vector\\\\n# clear\\\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\\\n    pass\\\\n# materials\\\\ndef mat(name,color,metal=0,rough=.4):\\\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\\\n# helpers\\\\ndef cube(n,loc,scale,ma,bev=0):\\\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\\\n o.data.materials.append(ma);return o\\\\ndef uv(n,loc,scale,ma):\\\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\\\ndef cyl(n,loc,r,dep,ma,verts=32):\\\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\\\n# stage\\\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\\\n# Cornell fabric panels facing across\\\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\\\n# cloth swags on table\\\\nfor i in range(7):\\\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\\\n# TEAPOT body at hero\\\\ncx,cy=.15,.35\\\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\\\n# shoulder/lid\\\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\\\n# spout angled right/front using cone between points\\\\ndef cone_between(n,a,b,r1,r2,ma):\\\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\\\n# handle torus behind\\\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\\\n# bunny left: body, haunch, head, ears\\\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\\\n# Suzanne bust right rear\\\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\\\n# checker sphere foreground right nodes\\\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\\\n# candlestick left front\\\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\\\n# orange fruit\\\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\\\n# open book right\\\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\\\n# ground rug neutral\\\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\\\n# camera\\\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\\\ntrack(cam,(0,0,1.35))\\\\n# light single warm shaft\\\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\\\n# world\\\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\\\n# settings\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\\\n# camera DOF\\\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_pcdGD8DIsErWadSESCdy7w1o\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 69, in <module>\\\\nTypeError: bpy_struct: item.attr = val: enum \\\\\\\"BLENDER_EEVEE_NEXT\\\\\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene\\\\nsc.render.engine='BLENDER_EEVEE'\\\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\\\nif cam:\\\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_kzbbnenHNiRLt0EPETYzrO3B\",\"content\":\"{\\n  \\\"status\\\": \\\"error\\\",\\n  \\\"message\\\": \\\"Traceback (most recent call last):\\\\n  File \\\\\\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\.runner\\\\\\\\blender-mcp-v1.0.0\\\\\\\\addon\\\\\\\\mcp_to_blender_server.py\\\\\\\", line 230, in _execute_code\\\\n    exec(code, namespace)\\\\n    ~~~~^^^^^^^^^^^^^^^^^\\\\n  File \\\\\\\"<string>\\\\\\\", line 11, in <module>\\\\nRuntimeError: Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\\n\\\",\\n  \\\"stdout\\\": \\\"Error: Cannot open file C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\tmp\\\\\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\\\nexcept: pass\\\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_YdcfrWdF6ajaYopKxbyZMEKV\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"objects\\\": 40,\\n    \\\"file\\\": \\\"C:\\\\\\\\Users\\\\\\\\proud\\\\\\\\Downloads\\\\\\\\Telegram Desktop\\\\\\\\blender-bench-runner-windows\\\\\\\\apps\\\\\\\\console\\\\\\\\classical_graphics_still_life.blend\\\",\\n    \\\"render\\\": \\\"//stilllife.png\\\"\\n  },\\n  \\\"stdout\\\": \\\"Info: Saved as \\\\\\\"classical_graphics_still_life.blend\\\\\\\"\\\\n\\\",\\n  \\\"stderr\\\": \\\"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\\\\\"//\\\\\\\" prefix\\\\n\\\"\\n}\"},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_hAGSOWw9upfhn9ZHzUTM9aO6\",\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"arguments\":\"{\\\"size_limit_in_bytes\\\":200000}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_hAGSOWw9upfhn9ZHzUTM9aO6\",\"content\":\"\"},{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,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\"}}]},{\"role\":\"assistant\",\"content\":null,\"tool_calls\":[{\"id\":\"call_CUnbLXuMmDt1iUcfXfm9Slbe\",\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"arguments\":\"{\\\"code\\\":\\\"import bpy\\\\n# switch current area to image editor for validation\\\\nif bpy.context.area: bpy.context.area.type='IMAGE_EDITOR'; bpy.context.area.spaces.active.image=bpy.data.images.get('Render Result')\\\\nresult={'rendered':bpy.data.images.get('Render Result') is not None}\\\"}\"}}]},{\"role\":\"tool\",\"tool_call_id\":\"call_CUnbLXuMmDt1iUcfXfm9Slbe\",\"content\":\"{\\n  \\\"status\\\": \\\"ok\\\",\\n  \\\"result\\\": {\\n    \\\"rendered\\\": true\\n  }\\n}\"}],\"usage\":{\"include\":true},\"tools\":[{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code\",\"description\":\"\\n        Execute Python code in the connected Blender instance.\\n\\n        The code runs in Blender's Python environment with full access to ``bpy``.\\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\\n        Deferred completion via ``check_is_finished`` is only supported by the\\n        interactive addon server, and is rejected in background mode.\\n        \",\"parameters\":{\"properties\":{\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"code\"],\"title\":\"execute_blender_codeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"execute_blender_code_for_cli\",\"description\":\"\\n        Execute Python code in a background Blender process.\\n\\n        Opens *blend_file* with ``blender --background`` and runs *code*.\\n        Assign a dict to ``result`` to return data.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"},\"code\":{\"title\":\"Code\",\"type\":\"string\"}},\"required\":[\"blend_file\",\"code\"],\"title\":\"execute_blender_code_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks\",\"description\":\"\\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_datablocksArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_datablocks_for_cli\",\"description\":\"\\n        Return a data-block summary by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_datablocks_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files\",\"description\":\"\\n        Report external file references that are missing from disk\\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_missing_filesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_missing_files_for_cli\",\"description\":\"\\n        Report missing file references by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_missing_files_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries\",\"description\":\"\\n        Return a tree of directly and indirectly linked library files.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_of_linked_librariesArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_of_linked_libraries_for_cli\",\"description\":\"\\n        Return linked-library info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_of_linked_libraries_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info\",\"description\":\"\\n        Simple/fast access to the blend file's path, save status, age, and backups.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_path_infoArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_path_info_for_cli\",\"description\":\"\\n        Return path info by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_path_info_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess\",\"description\":\"\\n        Guess the primary use-cases of the current blend file (scored 0-100 with certainty).\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_blendfile_summary_usage_guessArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_blendfile_summary_usage_guess_for_cli\",\"description\":\"\\n        Guess use-cases by opening *blend_file* in background Blender.\\n        \",\"parameters\":{\"properties\":{\"blend_file\":{\"title\":\"Blend File\",\"type\":\"string\"}},\"required\":[\"blend_file\"],\"title\":\"get_blendfile_summary_usage_guess_for_cliArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_object_detail_summary\",\"description\":\"\\n        Return a structured summary of the object identified by *name*.\\n\\n        Includes type, transforms, parent, children, modifiers, constraints,\\n        materials, visibility, data-block name, and collections.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"get_object_detail_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_objects_summary\",\"description\":\"\\n        Return the scene's collection hierarchy and their objects.\\n\\n        Each collection lists its objects (name, type, parent, data name,\\n        selection, visibility) and nested child collections.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_objects_summaryArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_python_api_docs\",\"description\":\"\\n        Return the Blender Python API docs for *identifier*, or list\\n        modules matching a trailing-``*`` discovery pattern.\\n\\n        *identifier* should be a fully-qualified Python name (e.g.\\n        ``bpy.app`` or ``bpy.types.Scene.frame_current``).\\n        The trailing-``*`` forms are supported as discovery entry-points:\\n\\n        - ``*`` enumerates the top-level modules (``bpy``, ``bmesh``,\\n          ``mathutils``, ``gpu``, ...).\\n        - ``X.*`` enumerates the direct-child identifiers under the\\n          *X* namespace (``bpy.*`` -> ``bpy.app``, ``bpy.context``, ...).\\n\\n        Both return a ``namespace`` response even when ``X.rst`` would\\n        otherwise resolve to ``exact``; the ``.*`` form lets an agent\\n        force the child listing.\\n\\n        The response always carries ``kind``, ``found``, and ``identifier``.\\n        The remaining keys depend on ``kind``:\\n\\n        - ``\\\"exact\\\"`` (``found=True``): ``<identifier>.rst`` was read.\\n          Extra keys: ``content`` (RST text), ``examples``. When the\\n          file exceeds 32 KB, ``content`` is replaced with a dot-point\\n          summary of the file's top-level definitions (prefixed by a\\n          header noting the truncation) and ``examples`` is empty -\\n          re-query individual members for their rendered blocks.\\n        - ``\\\"namespace\\\"`` (``found=True``):\\n          no ``<identifier>.rst`` but ``<identifier>.<child>.rst`` siblings exist.\\n          Extra key: ``submodules`` (list of child identifiers).\\n        - ``\\\"definition\\\"`` (``found=True``):\\n          *identifier* is defined inside a parent RST\\n          (e.g. ``bpy.props.IntProperty`` lives in ``bpy.props.rst``).\\n          Extra keys: ``content`` (rendered block), ``examples``.\\n        - ``\\\"partial\\\"`` (``found=False``):\\n          the parent RST was located but the trailing component isn't defined in it.\\n          Extra keys:\\n          - ``parent`` the identifier whose RST was loaded.\\n          - ``available`` top-level definitions in that RST.\\n          - ``submodules`` sibling identifiers ``<parent>.<child>`` with their own RSTs,\\n            filtered to those whose last component contains every character of the missing tail.\\n\\n          For a toctree landing page like ``bpy.types`` ``available`` is empty and ``submodules``\\n          is the near-miss list; for a self-contained module like ``bpy.props`` it's the reverse.\\n        - ``\\\"suggestions\\\"`` (``found=False``):\\n          no direct match, but *identifier* appears as a component of other files.\\n          Extra key: ``suggestions`` (list of full identifiers).\\n        - ``\\\"missing\\\"`` (``found=False``): nothing matched.\\n\\n        ``examples`` (present on the ``exact`` and ``definition`` kinds)\\n        is a list of ``{path, content}`` entries referenced from this documentation.\\n        \",\"parameters\":{\"properties\":{\"identifier\":{\"title\":\"Identifier\",\"type\":\"string\"}},\"required\":[\"identifier\"],\"title\":\"get_python_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_area_as_image\",\"description\":\"\\n        Take a screenshot of a single Blender area and return it as a PNG image.\\n\\n        *area_ui_type* matches the area's ``ui_type``.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"area_ui_type\":{\"enum\":[\"VIEW_3D\",\"IMAGE_EDITOR\",\"UV\",\"ShaderNodeTree\",\"CompositorNodeTree\",\"GeometryNodeTree\",\"TextureNodeTree\",\"SEQUENCE_EDITOR\",\"CLIP_EDITOR\",\"DOPESHEET_EDITOR\",\"GRAPH_EDITOR\",\"NLA_EDITOR\",\"TEXT_EDITOR\",\"CONSOLE\",\"INFO\",\"TOPBAR\",\"STATUSBAR\",\"OUTLINER\",\"PROPERTIES\",\"FILE_BROWSER\",\"SPREADSHEET\",\"PREFERENCES\"],\"title\":\"Area Ui Type\",\"type\":\"string\"},\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"required\":[\"area_ui_type\"],\"title\":\"get_screenshot_of_area_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_image\",\"description\":\"\\n        Take a screenshot of the entire Blender window and return it as a PNG image.\\n\\n        *size_limit_in_bytes* caps the image size in bytes.\\n        Zero (the default) uses the MCP message size limit.\\n        \",\"parameters\":{\"properties\":{\"size_limit_in_bytes\":{\"default\":0,\"title\":\"Size Limit In Bytes\",\"type\":\"integer\"}},\"title\":\"get_screenshot_of_window_as_imageArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"get_screenshot_of_window_as_json\",\"description\":\"\\n        Return a JSON description of the Blender window layout, areas, active object, and selection.\\n        \",\"parameters\":{\"properties\":{},\"title\":\"get_screenshot_of_window_as_jsonArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_name\",\"description\":\"\\n        Switch the active workspace tab to *name*.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"}},\"required\":[\"name\"],\"title\":\"jump_to_tab_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_tab_by_space_type\",\"description\":\"\\n        Switch to a workspace whose main area matches *space_type*.\\n\\n        If *allow_edits* is True and no matching workspace exists, a new one\\n        is created by duplicating the current workspace.\\n        \",\"parameters\":{\"properties\":{\"space_type\":{\"title\":\"Space Type\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"space_type\"],\"title\":\"jump_to_tab_by_space_typeArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_by_name\",\"description\":\"\\n        Move the 3D viewport to focus on an object by *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"jump_to_view3d_object_data_by_name\",\"description\":\"\\n        Move the 3D viewport to the object whose data block matches *name*.\\n\\n        If *allow_edits* is True the object may be un-hidden and its\\n        collections enabled to make it visible.\\n        \",\"parameters\":{\"properties\":{\"name\":{\"title\":\"Name\",\"type\":\"string\"},\"allow_edits\":{\"default\":false,\"title\":\"Allow Edits\",\"type\":\"boolean\"}},\"required\":[\"name\"],\"title\":\"jump_to_view3d_object_data_by_nameArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_thumbnail_to_path\",\"description\":\"\\n        Render a small, low-quality thumbnail to *output_path* (temporarily overrides settings).\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_thumbnail_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"render_viewport_to_path\",\"description\":\"\\n        Render the current scene to *output_path* using current render settings.\\n        \",\"parameters\":{\"properties\":{\"output_path\":{\"title\":\"Output Path\",\"type\":\"string\"}},\"required\":[\"output_path\"],\"title\":\"render_viewport_to_pathArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_api_docs\",\"description\":\"\\nFull-text search over the bundled Blender Python API reference.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_api_docsArguments\",\"type\":\"object\"}}},{\"type\":\"function\",\"function\":{\"name\":\"search_manual_docs\",\"description\":\"\\nFull-text search over the bundled Blender user manual.\\n\\nReturns a ranked list of hits. Each hit has:\\n\\n- ``path``: file path relative to the bundled docs.\\n- ``text``: the matching paragraph plus ``context``\\n  paragraphs on either side.\\n- ``breadcrumb``: the section path containing the hit\\n  (``Section > Sub-section > ...``).\\n- ``index``: the hit's position in the result list.\\n- ``score``: a relevance score; higher is better.\\n\\nThe query is tokenised on whitespace and matched\\ncase-insensitively. Every token must appear somewhere in\\nthe paragraph body, the file path, or an enclosing section\\ntitle - in any order. Common English stop-words (``the``,\\n``a``, ``how``, ``to``, ...) are dropped, so natural\\nphrasings like ``\\\"how to bake\\\"`` work as expected. Regular\\nexpressions are not supported.\\n\\nUse ``context`` to pull more surrounding paragraphs into\\neach hit (symmetric, default 0). Use ``index`` with the\\nposition of a previous hit (same query) to get that hit\\nalone with its text widened to its enclosing section.\\n\\nRead-only; consults bundled RST files only.\\n\",\"parameters\":{\"properties\":{\"query\":{\"title\":\"Query\",\"type\":\"string\"},\"max_results\":{\"default\":20,\"title\":\"Max Results\",\"type\":\"integer\"},\"context\":{\"default\":0,\"title\":\"Context\",\"type\":\"integer\"},\"index\":{\"anyOf\":[{\"type\":\"integer\"},{\"type\":\"null\"}],\"default\":null,\"title\":\"Index\"}},\"required\":[\"query\"],\"title\":\"search_manual_docsArguments\",\"type\":\"object\"}}}]}", "body_base64": "{"model":"openai/gpt-5.6-luna","messages":[{"role":"system","content":"You are participating in Blender-Bench, a public benchmark where AI models complete the same Blender task.\n\nYour result will be compared head-to-head with another model. Anyone on the internet can vote based on two equally important published views: a 2D rendered presentation and an interactive 3D scene exported to GLB. Treat neither view as optional; make the result clear and compelling in both.\n\nComplete the request as well as you can. The benchmark does not prescribe a workflow or visual style; use whichever Blender techniques you consider appropriate. Make the important requirements evident in the published result, save a complete scene, and present the work clearly in every requested output.\n\nThe 2D presentation is rendered by the benchmark from your saved scene using Cycles at 1536 x 1536, 64 samples, denoising enabled, and GPU acceleration when available with a CPU fallback. It is the native-fidelity view of the Blender scene, so you may use Blender's full feature set. Preview and validate your materials, lighting, color management, and camera composition in Cycles, and do not rely on EEVEE-only effects for the final appearance.\n\nThe interactive 3D view is a best-effort GLB export containing geometry, compatible materials, the contestant camera, and supported lights. Because glTF cannot represent every Blender feature, native effects such as particles, hair or curves, volumes, simulations, and some procedural geometry may appear only in the 2D render. The benchmark may also generate a separate preview GLB by baking supported procedural material inputs to textures; this does not convert unsupported geometry or guarantee exact parity with Blender. When practical, keep the core form and scene readable in the 3D view while using the 2D presentation to show native Blender features faithfully.\n\n## Harness Instructions\n\nYou are operating Blender through the provided tools. Tool calls execute one at a time, in the order you issue them, against a single live Blender instance — each call sees the state left by the previous one, and issuing several calls in one message gains no parallelism. The scene starts empty. Build what the user's prompt asks for in the current scene. Work step by step and verify your own work.\n\nYou are building a real three-dimensional scene, and evaluators judge the whole scene and your final render side by side — an interactive 3D view and a benchmark-generated 360° camera orbit rendered in Cycles from your saved scene appear next to your presentation render, so forms that only read correctly from one angle will show. Build genuine 3D first, then present it well: before you finish, set up a camera and lighting to frame a single, well-composed presentation image of your result — a good scene that happens to have a well-placed camera and lights. The final benchmark capture overrides the scene render settings and renders in Cycles at 1536x1536, 64 samples, with denoising and GPU acceleration (CPU fallback). Preview and validate materials, lighting, color management, and composition in Cycles; do not rely on EEVEE-only effects. Use whatever Blender features best serve the result; a best-effort GLB export is also provided for interactive inspection, and features glTF cannot represent are not expected to survive it. When you are finished, reply with the single word DONE and no tool calls."},{"role":"user","content":"## Task\n\nComplete the following Blender task:\n\n<task>\n\nA classical still life in the Dutch Golden Age manner — dark ground, draped cloth, a shaft of warm light, objects arranged with deliberate care — except every object on the table is a monument of computer-graphics history. The twist must work in both directions: read as a museum-worthy still life at arm's length, and land as one long graphics in-joke on the second look.\n\nThe cast (required objects, all modeled in-scene — no imported meshes, no downloaded assets).\n- The Utah Teapot, hero of the arrangement: the classic squat proportions with the familiar spout, handle, and lid. Give it an honest ceramic or silver-teapot material worthy of a Vermeer table.\n- The Stanford Bunny, approximated: the recognizable compact crouched-rabbit silhouette with its rounded back and folded ears, at rest on the table like a sculpture. A faithful sculptural likeness of the pose is what counts, not vertex-level accuracy.\n- Suzanne, Blender's own monkey head, mounted or resting as a bust among the objects — she may be the one \"portrait\" gazing out of the arrangement.\n- A reflective checkered sphere — the ray-tracing demo classic — polished enough to catch the rest of the arrangement in its reflection.\n- At least one supporting classical prop to sell the genre: fruit, a candlestick, an open book, a wine glass — pick what the composition needs.\n\nThe Cornell nod. Work the Cornell box palette into the scene's fabric: one strong red and one strong green element facing each other across the composition — draped cloths, wall panels, or book covers — with neutral white/grey between them, so the whole frame quietly becomes the box.\n\nMaterials & light. Chiaroscuro is the assignment: a single dominant warm light source (window shaft or candle) from one side, deep soft shadow elsewhere, a dark background that lets the objects emerge. Materials do the storytelling — glazed ceramic, patinated metal, velvet-dark cloth with real fold structure, polished chrome on the sphere. Everything slightly aged, nothing laboratory-clean.\n\nComposition. A table edge in the lower frame, objects grouped in overlapping depth with the teapot commanding the arrangement, camera at the intimate slightly-elevated angle of the genre, roughly 50mm equivalent. The image should hold together as one calm, deliberate painting.\n\n</task>"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_pcdGD8DIsErWadSESCdy7w1o","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy, math\\nfrom mathutils import Vector\\n# clear\\nbpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete(use_global=False)\\nfor d in (bpy.data.materials, bpy.data.curves, bpy.data.meshes, bpy.data.cameras, bpy.data.lights):\\n    pass\\n# materials\\ndef mat(name,color,metal=0,rough=.4):\\n m=bpy.data.materials.new(name); m.diffuse_color=(*color,1); m.use_nodes=True; bs=m.node_tree.nodes.get('Principled BSDF'); bs.inputs['Base Color'].default_value=(*color,1); bs.inputs['Roughness'].default_value=rough; bs.inputs['Metallic'].default_value=metal; return m\\nblack=mat('Deep Walnut',(0.025,.018,.015),0,.3); wood=mat('Aged Table',(0.12,.045,.018),0,.32); white=mat('Ivory Ceramic',(.82,.78,.68),0,.2); silver=mat('Patinated Silver',(.35,.4,.4),.75,.18); red=mat('Cornell Crimson',(.38,.015,.012),0,.55); green=mat('Cornell Green',(.015,.22,.045),0,.6); gold=mat('Warm Brass',(.5,.2,.04),.75,.22); paper=mat('Old Paper',(.58,.48,.3),0,.8); fruitmat=mat('Fruit Orange',(.55,.07,.015),0,.28)\\n# helpers\\ndef cube(n,loc,scale,ma,bev=0):\\n bpy.ops.mesh.primitive_cube_add(location=loc); o=bpy.context.object;o.name=n;o.scale=scale; bpy.ops.object.transform_apply(location=False,rotation=False,scale=True)\\n if bev: mod=o.modifiers.new('Soft edges','BEVEL');mod.width=bev;mod.segments=3\\n o.data.materials.append(ma);return o\\ndef uv(n,loc,scale,ma):\\n bpy.ops.mesh.primitive_uv_sphere_add(segments=40, ring_count=24, location=loc);o=bpy.context.object;o.name=n;o.scale=scale;bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);o.data.materials.append(ma);bpy.ops.object.shade_smooth();return o\\ndef tor(n,loc,major,minor,ma,rot=(0,0,0)):\\n bpy.ops.mesh.primitive_torus_add(major_radius=major,minor_radius=minor,major_segments=40,minor_segments=12,location=loc,rotation=rot);o=bpy.context.object;o.name=n;o.data.materials.append(ma);return o\\ndef cyl(n,loc,r,dep,ma,verts=32):\\n bpy.ops.mesh.primitive_cylinder_add(vertices=verts,radius=r,depth=dep,location=loc);o=bpy.context.object;o.name=n;o.data.materials.append(ma);be=o.modifiers.new('Rounded','BEVEL');be.width=.04;be.segments=2;return o\\n# stage\\ncube('Table',(0,0,0),(4.8,3.2,.28),wood,.12); cube('Back wall',(0,3.25,3),(4.8,.18,3),black,.05)\\n# Cornell fabric panels facing across\\ncube('Red Draped Cloth',(-3.55,2.85,2.25),(1.0,.12,2.0),red,.18); cube('Green Draped Cloth',(3.55,2.85,2.25),(1.0,.12,2.0),green,.18)\\n# cloth swags on table\\nfor i in range(7):\\n x=-3.3+i*.32; z=.38+ .12*math.sin(i*1.3); cube('Red velvet fold', (x,-2.25,z),(.22,.55,.18),red,.14)\\n# TEAPOT body at hero\\ncx,cy=.15,.35\\nuv('Teapot squat body',(cx,cy,1.45),(1.18,1.0,.82),silver)\\n# shoulder/lid\\nuv('Teapot shoulder',(cx,cy,2.05),(.72,.65,.25),silver); cyl('Teapot lid rim',(cx,cy,2.22),.52,.10,silver); uv('Teapot lid knob',(cx,cy,2.38),(.16,.16,.12),silver)\\n# spout angled right/front using cone between points\\ndef cone_between(n,a,b,r1,r2,ma):\\n a=Vector(a);b=Vector(b);mid=(a+b)/2; d=b-a\\n bpy.ops.mesh.primitive_cone_add(vertices=32,radius1=r1,radius2=r2,depth=d.length,location=mid);o=bpy.context.object;o.name=n;o.data.materials.append(ma);o.rotation_mode='QUATERNION';o.rotation_quaternion=d.to_track_quat('Z','Y');return o\\ncone_between('Teapot spout',(1.0,.35,1.65),(1.9,.25,2.18),.38,.13,silver); tor('Spout lip',(1.93,.25,2.2),.14,.05,silver,rot=(0,math.pi/2-.45,0))\\n# handle torus behind\\nbpy.ops.mesh.primitive_torus_add(major_radius=.78,minor_radius=.12,major_segments=40,minor_segments=12,location=(-.62,.45,1.55),rotation=(math.pi/2,0,0));h=bpy.context.object;h.name='Teapot handle';h.scale=(1,.8,1);h.data.materials.append(silver)\\n# bunny left: body, haunch, head, ears\\nuv('Stanford Bunny body',(-2.05,.5,1.0),(.8,.55,.65),white);uv('Bunny haunch',(-2.48,.48,.83),(.55,.5,.55),white);uv('Bunny head',(-1.72,.43,1.48),(.48,.42,.48),white)\\nfor x,ang in [(-1.9,-.12),(-1.55,.12)]:\\n e=uv('Bunny folded ear',(x,.43,2.02),(.16,.22,.62),white);e.rotation_euler[1]=ang\\nuv('Bunny muzzle',(-1.55,.1,1.4),(.23,.2,.18),white);uv('Bunny tail',(-2.65,.72,1.15),(.25,.22,.25),white)\\n# Suzanne bust right rear\\nbpy.ops.mesh.primitive_monkey_add(location=(2.05,.75,1.45)); su=bpy.context.object;su.name='Suzanne Bust';su.scale=(.72,.62,.72);bpy.ops.object.transform_apply(location=False,rotation=False,scale=True);su.data.materials.append(gold);bpy.ops.object.shade_smooth();sub=su.modifiers.new('Sculptural smooth','SUBSURF');sub.levels=2;sub.render_levels=2\\ncyl('Bust pedestal',(2.05,.75,.75),.6,.35,black)\\n# checker sphere foreground right nodes\\nbpy.ops.mesh.primitive_uv_sphere_add(segments=48, ring_count=32, location=(2.55,-1.15,1.05), radius=.72);sp=bpy.context.object;sp.name='Reflective Checkered Sphere';sp.data.materials.append(silver);bpy.ops.object.shade_smooth()\\nm=bpy.data.materials.new('Ray Tracing Checker');m.use_nodes=True;n=m.node_tree.nodes;links=m.node_tree.links;bs=n.get('Principled BSDF');tex=n.new('ShaderNodeTexChecker');tex.inputs['Color1'].default_value=(.03,.03,.03,1);tex.inputs['Color2'].default_value=(.85,.85,.8,1);tex.inputs['Scale'].default_value=7;links.new(tex.outputs['Color'],bs.inputs['Base Color']);bs.inputs['Metallic'].default_value=.85;bs.inputs['Roughness'].default_value=.12;sp.data.materials.clear();sp.data.materials.append(m)\\n# candlestick left front\\ncyl('Candlestick base',(-3,-.8,.62),.48,.18,gold);cyl('Candlestick stem',(-3,-.8,1.15),.12,.9,gold);tor('Candle tray',(-3,-.8,1.55),.32,.08,gold);cyl('Candle',(-3,-.8,1.85),.16,.55,white);uv('Flame',(-3,-.8,2.22),(.1,.1,.2),gold)\\n# orange fruit\\nuv('Still life apple',(-.75,-1.45,.78),(.48,.48,.45),fruitmat);cyl('Apple stem',(-.75,-1.45,1.2),.05,.22,wood)\\n# open book right\\ncube('Open Book',(3.45,-.7,.58),(1.0,.72,.08),paper,.04);cube('Book spine',(3.45,-.7,.7),(.08,.72,.07),wood,.02)\\n# ground rug neutral\\ncube('Neutral cloth',(0,0,.42),(4.3,2.5,.04),mat('Neutral Grey',(.16,.14,.12),0,.9),.05)\\n# camera\\nbpy.ops.object.camera_add(location=(8,-12,7),rotation=(math.radians(67),0,math.radians(34)));cam=bpy.context.object;cam.name='Presentation Camera';bpy.context.scene.camera=cam;cam.data.lens=52\\ndef track(o,pt): o.rotation_euler=(Vector(pt)-o.location).to_track_quat('-Z','Y').to_euler()\\ntrack(cam,(0,0,1.35))\\n# light single warm shaft\\nbpy.ops.object.light_add(type='AREA', location=(-4,-4,7));L=bpy.context.object;L.name='Warm Window Shaft';L.data.energy=1050;L.data.shape='DISK';L.data.size=4;L.data.color=(1.0,.55,.28);track(L,(0,0,1))\\n# world\\nw=bpy.context.scene.world;w.color=(.005,.003,.002);w.use_nodes=True;w.node_tree.nodes['Background'].inputs['Color'].default_value=(.006,.004,.003,1);w.node_tree.nodes['Background'].inputs['Strength'].default_value=.12\\n# settings\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE_NEXT';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\nsc.view_settings.look='AgX - Medium High Contrast';sc.render.image_settings.color_mode='RGBA'\\n# camera DOF\\ncam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects['Teapot squat body'];cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(bpy.context.scene.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_pcdGD8DIsErWadSESCdy7w1o","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 69, in <module>\\nTypeError: bpy_struct: item.attr = val: enum \\\"BLENDER_EEVEE_NEXT\\\" not found in ('BLENDER_EEVEE', 'BLENDER_WORKBENCH', 'CYCLES')\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_kzbbnenHNiRLt0EPETYzrO3B","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene\\nsc.render.engine='BLENDER_EEVEE'\\nsc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100\\nsc.render.image_settings.file_format='PNG';sc.render.filepath='/tmp/stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera'); sc.camera=cam\\nif cam:\\n cam.data.dof.use_dof=True;cam.data.dof.focus_object=bpy.data.objects.get('Teapot squat body');cam.data.dof.aperture_fstop=5\\nbpy.ops.wm.save_as_mainfile(filepath='/tmp/classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'path':'/tmp/classical_graphics_still_life.blend'}\"}"}}]},{"role":"tool","tool_call_id":"call_kzbbnenHNiRLt0EPETYzrO3B","content":"{\n  \"status\": \"error\",\n  \"message\": \"Traceback (most recent call last):\\n  File \\\"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\.runner\\\\blender-mcp-v1.0.0\\\\addon\\\\mcp_to_blender_server.py\\\", line 230, in _execute_code\\n    exec(code, namespace)\\n    ~~~~^^^^^^^^^^^^^^^^^\\n  File \\\"<string>\\\", line 11, in <module>\\nRuntimeError: Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\\n\",\n  \"stdout\": \"Error: Cannot open file C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\tmp\\\\classical_graphics_still_life.blend@ for writing: No such file or directory\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_YdcfrWdF6ajaYopKxbyZMEKV","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\nsc=bpy.context.scene;sc.render.engine='BLENDER_EEVEE';sc.render.resolution_x=700;sc.render.resolution_y=700;sc.render.resolution_percentage=100;sc.render.filepath='//stilllife.png';sc.render.film_transparent=False\\ntry: sc.view_settings.look='AgX - Medium High Contrast'\\nexcept: pass\\ncam=bpy.data.objects.get('Presentation Camera');sc.camera=cam\\nbpy.ops.wm.save_as_mainfile(filepath='//classical_graphics_still_life.blend');bpy.ops.render.render(write_still=True)\\nresult={'objects':len(sc.objects),'file':bpy.data.filepath,'render':sc.render.filepath}\"}"}}]},{"role":"tool","tool_call_id":"call_YdcfrWdF6ajaYopKxbyZMEKV","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"objects\": 40,\n    \"file\": \"C:\\\\Users\\\\proud\\\\Downloads\\\\Telegram Desktop\\\\blender-bench-runner-windows\\\\apps\\\\console\\\\classical_graphics_still_life.blend\",\n    \"render\": \"//stilllife.png\"\n  },\n  \"stdout\": \"Info: Saved as \\\"classical_graphics_still_life.blend\\\"\\n\",\n  \"stderr\": \"<string>:6: RuntimeWarning: WM_OT_save_as_mainfile.filepath: does not support blend relative \\\"//\\\" prefix\\n\"\n}"},{"role":"assistant","content":null,"tool_calls":[{"id":"call_hAGSOWw9upfhn9ZHzUTM9aO6","type":"function","function":{"name":"get_screenshot_of_window_as_image","arguments":"{\"size_limit_in_bytes\":200000}"}}]},{"role":"tool","tool_call_id":"call_hAGSOWw9upfhn9ZHzUTM9aO6","content":""},{"role":"user","content":[{"type":"image_url","image_url":{"url":"data:image/png;base64,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"}}]},{"role":"assistant","content":null,"tool_calls":[{"id":"call_CUnbLXuMmDt1iUcfXfm9Slbe","type":"function","function":{"name":"execute_blender_code","arguments":"{\"code\":\"import bpy\\n# switch current area to image editor for validation\\nif bpy.context.area: bpy.context.area.type='IMAGE_EDITOR'; bpy.context.area.spaces.active.image=bpy.data.images.get('Render Result')\\nresult={'rendered':bpy.data.images.get('Render Result') is not None}\"}"}}]},{"role":"tool","tool_call_id":"call_CUnbLXuMmDt1iUcfXfm9Slbe","content":"{\n  \"status\": \"ok\",\n  \"result\": {\n    \"rendered\": true\n  }\n}"}],"usage":{"include":true},"tools":[{"type":"function","function":{"name":"execute_blender_code","description":"\n        Execute Python code in the connected Blender instance.\n\n        The code runs in Blender's Python environment with full access to ``bpy``.\n        To return data, assign a JSON-serialisable dict to a variable named ``result``.\n        Deferred completion via ``check_is_finished`` is only supported by the\n        interactive addon server, and is rejected in background mode.\n        ","parameters":{"properties":{"code":{"title":"Code","type":"string"}},"required":["code"],"title":"execute_blender_codeArguments","type":"object"}}},{"type":"function","function":{"name":"execute_blender_code_for_cli","description":"\n        Execute Python code in a background Blender process.\n\n        Opens *blend_file* with ``blender --background`` and runs *code*.\n        Assign a dict to ``result`` to return data.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"},"code":{"title":"Code","type":"string"}},"required":["blend_file","code"],"title":"execute_blender_code_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks","description":"\n        Return a summary of the blend file: data-block counts, active workspace, and render engine.\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_datablocksArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_datablocks_for_cli","description":"\n        Return a data-block summary by opening *blend_file* in background Blender.\n        ","parameters":{"properties":{"blend_file":{"title":"Blend File","type":"string"}},"required":["blend_file"],"title":"get_blendfile_summary_datablocks_for_cliArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files","description":"\n        Report external file references that are missing from disk\n        (images, libraries, fonts, sounds, movie clips, caches, sequences).\n        ","parameters":{"properties":{},"title":"get_blendfile_summary_missing_filesArguments","type":"object"}}},{"type":"function","function":{"name":"get_blendfile_summary_missing_files_for_cli","description":"\n        Report missing file references by opening *blend_file* in 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