The repository is called blender-ai-mcp-addon. The product is called mcp-for-blender-pro. It was created on 15 October, tagged v1.0.0 the same day, and has 728 stars and 319 forks three days later. Licence is MIT; the project states it is independent and not affiliated with or endorsed by the Blender Foundation or any of the AI providers it connects to.
It is the second wave of Blender-plus-LLM tooling: not a bare MCP bridge, but an addon with a UI, a model picker, an approval flow and a task board.
What it does
Three things stacked on each other.
A chat workspace inside Blender. Blender 4.2+, installed as an extension from a zip. You get a panel under 3D Viewport → N → MCP Pro and a companion “Studio” window holding chat, a scene-object list, viewport snapshots, approvals and the task board. You keep working in Blender while it runs.
21 scene tools. Create, inspect, edit, capture, search assets, consult Blender APIs and export. Scene and object inspection runs before the model acts, which is the part that distinguishes this from pasting bpy snippets into a chat window — the assistant gets context about what is actually in the file.
A built-in MCP server. Authenticated Streamable HTTP, so external clients — OpenCode is named — can drive the same tools from outside Blender.
The agent-team part is the unusual bit
Most of these tools give you one assistant. This one gives you a configurable team chain, each role with its own instructions and its own model:
- Manager — breaks the request into assignments with acceptance criteria.
- Workers — perform the assigned scene operations.
- Reviewers — inspect results and either approve or return specific findings.
Tasks run sequentially so scene edits stay ordered. A blocked task gets one correction attempt; a second failed review stops the workflow. The task board exposes assignments, attempts and review findings as work proceeds.
That failure policy is the detail worth noticing. An agent loop with no attempt cap is how you end up with 400 modifier operations and a scene you cannot undo. Capping retries at one and halting is a deliberately conservative choice, and it is the right one for software that mutates a file you care about.
Model choice, including local
Supported connections: OpenAI, Anthropic Claude, Google Gemini, OpenRouter, OpenCode Zen, Ollama and custom APIs. Custom endpoints speak OpenAI Chat Completions, Anthropic Messages or Ollama protocols. You load the provider’s model catalogue and pick inside Blender.
Two constraints the docs state plainly: choose models with tool-calling support, and only image-capable models can interpret the viewport screenshots — which is the feature that makes a reviewer role meaningful at all. Saved connections keep work and personal credentials separate, including multiple keys for one provider. Menus are available in English, German, French, Spanish and Chinese.
The honest caveats, quoted
The README does something most addon READMEs do not, which is tell you where the safety stops:
Python tool validation helps catch disallowed operations; it is not a security sandbox.
And on state: built-in chat supports operation approvals, cancellation after the current step, and named undo steps for mutations. External MCP clients manage their own approval policies. Credentials live in your user settings. Chat history lasts for the current session only.
Read that stack honestly. You are giving a language model the ability to execute Python inside your Blender process, with a validator the authors describe as a filter rather than a jail, and when you expose the MCP server the approval policy becomes whatever the connecting client decides. For a scratch file that is fine. For a production scene with linked libraries, make a copy.
Two things to go in with your eyes open about
It is Windows-only in practice. The source is MIT and the addon is Python, but there is exactly one release asset: mcp-for-blender-pro.zip, 114 MB, badged Windows x64, with 679 downloads at the time of writing. If you are on macOS or Linux you are building from the repository, and nothing in the README tells you how.
The fork count is odd. 728 stars and 319 forks in three days is a ratio of 0.44. Healthy addons of this age usually sit an order of magnitude below that. We are not alleging anything — it may be people grabbing a copy of a 114 MB release’s source tree — but it is worth knowing that the star count is not a clean popularity signal here.
There is also a keyword block in the README running to roughly forty SEO terms, from “blender copilot” to “geometry nodes”. That is a marketing choice, not a code smell, but it does tell you how the project expects to be found.
Who it is for
Artists who want faster iteration on layout and material busywork, technical artists automating repetitive passes, and developers wiring AI clients to Blender. The example the project gives — inspect the scene, build a product-display layout, arrange objects, assign materials, prepare an export — is exactly the kind of task that is tedious, well-specified and easy to check by looking at it. That is the shape of work this is good at.
What it is not is a modelling assistant. Nothing here sculpts, retopologises or art-directs. It operates the application.
Sources
- LiCinCan/blender-ai-mcp-addon — GitHub
- README — capabilities, agent roles, caveats and licence
- v1.0.0 release — GitHub
- Model Context Protocol specification