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name: scenario-blender description: "Use when an agent works in an open Blender scene through the Scenario for Blender extension's local MCP server (scenario-blender on 127.0.0.1:9876): connecting Claude Code, Cursor, Claude Desktop or Codex to Blender, the headless scenario_blender command, quoting and generating Scenario images, PBR materials, 3D meshes, video, audio or 360 environments from the scene, capturing the viewport or selected meshes as references, placing saved results, Render Video camera paths, or execute_python." license: MIT

Scenario for Blender

Overview

Scenario for Blender is an open-source extension for Blender 5.0 and later, licensed GPL-3.0-or-later and marked experimental in its README. Users install it from the extension repository https://blender.scenario.com/repo/index.json (Edit > Preferences > Get Extensions > Repositories) or from a ZIP on the releases page; the handbook is blender.scenario.com. The Scenario API key and secret live in the extension's preferences: the agent never handles them. Blender's Allow Online Access must be on.

The extension runs a local MCP server, scenario-blender, at http://127.0.0.1:9876/mcp (JSON-RPC over HTTP POST with a bearer token, no SSE). When 9876 is busy it tries the next three ports, so the URL to use is the one shown in Scenario > Agents (MCP). Scene tools run on Blender's main thread. Every tool and parameter here comes from the extension's tool reference, docs/MCP.md; tools/list carries each tool's full guidance. Local names are verb-first (list_models, job_status) and never interchangeable with the hosted server's (models_list, model_run, jobs_wait). Training, collection rename or deletion, workflow authoring and usage stay on mcp.scenario.com: see the scenario skill, and connect both servers when a task needs both. Modeling, sculpting, rigging or shading through bpy is the scenario-blender-expert skill's work; exporting for an engine is scenario-blender-export's. If a sibling skill named here is missing from your available skills, ask the user to install it (npx skills add scenario-labs/skills --skill <name>); unattended, proceed from tool schemas and flag the gap.

Setup

The token is generated the first time the server starts in a Blender session, held only in memory, and changes after every restart: a 401 means it is stale, so copy the setup again from the Agents (MCP) panel. It is a secret, separate from the API key: never paste it, or a copied snippet, into an issue, transcript or screenshot. The client must run on the same machine, since 127.0.0.1 is loopback, and must use that address: another host name returns 403 forbidden host.

Claude Code:

claude mcp add --transport http scenario-blender http://127.0.0.1:9876/mcp \
  --header "Authorization: Bearer <token>"

Codex:

export SCENARIO_BLENDER_TOKEN='<token>'
codex mcp add scenario-blender --url http://127.0.0.1:9876/mcp \
  --bearer-token-env-var SCENARIO_BLENDER_TOKEN

Cursor takes an mcpServers entry "scenario-blender": {"url": "http://127.0.0.1:9876/mcp", "headers": {"Authorization": "Bearer <token>"}}. Claude Desktop goes through the extension's stdio bridge: copy its snippet from the panel, which fills in the installed shim path, and check that its command points to a working Python 3 interpreter.

Headless, with the extension enabled:

export SCENARIO_BLENDER_TOKEN='<token>'
blender --background scene.blend --command scenario_blender --port 9876

--online-mode enables online access for that run. --token '<token>' overrides the variable but exposes the token in process arguments; with neither, the command generates one and prints it once. Headless, screenshot_viewport, render_still and every capture_reference source except MESH fail: they need the GUI.

Quick reference

In call order:

| Step | Tool | Notes | | ---------------- | ------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | Read the scene | scene_summary, datablocks_summary, object_detail(name) | Start here; scene_summary reports the selection, cameras, frame range and 3D cursor | | Change the scene | select_objects(names), set_frame(frame), camera_path | camera_path takes a preset (such as orbit or push_in), a free-text description or waypoints, plus duration, focal, aim_at_subject; it animates the scene camera for Render Video and generates nothing | | Look | screenshot_viewport, render_still(source, width, height) | GUI only; PNG content for the agent, not an upload | | Reference | capture_reference(source), then reference_upload_status(context_id, reference_id) | source: VIEWPORT, CAMERA, VIEWPORT_CLIP, CAMERA_CLIP or MESH (selected meshes as one GLB). Poll until state is imported, then use its asset_id | | Pick a model | list_models(lane, query), model_schema(model_id) | Lanes: image, video, 3d, material, audio, render_image, render_video, edit3d, panorama. Ids come from list_models each session, never constants | | Quote | estimate_cost(model_id, parameters, lane) | Free dry run; returns quote_id and cu_cost_exact (a decimal string). lane defaults to image | | Generate | generate(lane, model_id, parameters, quote_id, approved_cost) | Spends credits. Same lane, model and parameters as the quote; approved_cost is the exact string the user approved | | Wait | wait_for_job(job_id, timeout), job_status(job_id) | timeout defaults to 45 seconds, inside the 60-second limit many clients apply; go up to 170 only when your client waits longer. Call again while delivery_active is true | | Place | list_local_jobs, prepare_result_application, then apply_result_application(context_id, application_id) | Only Image results import by themselves; everything else waits for an approved destination | | Python | blender_api_help(path), then execute_python(code) | Off until the user enables Allow connected agents to run Python |

upload_reference(path, kind) sends only a file the user chose in Blender this session or one the extension saved; any other path is refused, so capture the scene instead or ask the user to pick the file. Render Image and Render Video take no raw parameters: render_form with action: "configure", then action: "prepare" captures and uploads the scene, then estimate_cost and generate with parameters omitted. The model copies the capture's motion, limbs included: for characters that must walk or run naturally, block them as capsules (scenario-blender-blockout). A panorama goes into the World through prepare_result_application with purpose: "world".

Worked example: viewport to a mesh at the 3D cursor

"Turn what I see in the viewport into a 3D prop next to it."

1. scene_summary: note cursor and confirm a GUI session, since the capture needs one.
2. capture_reference with {"source": "VIEWPORT"} returns context_id and reference_id, not an asset id. Call reference_upload_status with both until state is imported, and keep its asset_id. A scene change before the upload finishes stops it.
3. list_models with {"lane": "3d"}; pick an image-to-3D entry from its description. model_schema with its model_id names the image input; file parameters take Scenario asset ids.
4. estimate_cost with {"lane": "3d", "model_id": "<id>", "parameters": {"<image input>": "<asset_id>"}} returns quote_id and cu_cost_exact. Tell the user the cost in CU and stop until they say yes. Unattended, with nobody to answer, report the quote and stop here: neither this gate nor the one in step 8 is ever passed without the user.
5. generate with the same lane, model_id and parameters, plus quote_id and approved_cost set to cu_cost_exact verbatim. It returns local_id.
6. wait_for_job with {"job_id": "<local_id>"} (45 seconds by default), again while delivery_active is true. The 3D lane stops at saved results: nothing is in the scene yet.
7. job_status lists import_units; take the key of the primary unit. list_local_jobs gives context_id and this job's request_id and revision.
8. prepare_result_application with {"context_id": "...", "request_id": "...", "expected_revision": <revision>, "asset_id": "<key>"} captures the current 3D cursor as the destination and returns application_id. Show the user the returned scene, cursor, format and files, and wait for approval.
9. apply_result_application with context_id and application_id imports one new model group at that cursor. scene_summary confirms it.

Common mistakes

• generate without a fresh estimate_cost and the user's yes. A changed lane, model, input, scene or credential needs a new quote, a quote is used once, and an estimate never authorizes spending by itself.
• Repeating a timed-out generate. Inspect job_status or list_local_jobs instead; an unconfirmed submission blocks an identical one until the user marks it reviewed in Blender.
• Passing a file path, or capture_reference's reference_id, where a file parameter takes an asset id.
• Expecting import_result to place a result: it only explains the approval flow above.
• Captures in --background: only capture_reference MESH works there.
• Assuming execute_python is on. It fails with PermissionError naming Scenario preferences (MCP > Allow connected agents to run Python), off by default: ask the user, and prefer the specific tools.
• Calling the hosted server's tools (model_run, jobs_wait, model_schema_get) on this server.
• Treating "catalog is still lo