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ellmos Blender Use MCP

🇩🇪 Deutsche Version

Part of the ellmos-ai family.

📦 View on npm →

An asset-QA tool for game and 3D asset pipelines: verify that an exported FBX actually reimports cleanly in headless Blender — mesh count, material count, and required naming prefixes checked automatically, with a deterministic JSON result instead of a manual eyeball pass. blender_verify_fbx_reimport is the core tool; blender_locate and blender_run_script are the general-purpose primitives it is built on.

No add-on. No TCP port. No background daemon. This server does not install anything into Blender, does not open a socket for a running Blender instance to connect to, and does not keep Blender resident. Each call spawns blender --background --python <script.py>, waits for a bounded, timeout-guarded exit, and returns the result — headless and stateless by design. It does not download assets and does not collect telemetry.

How this differs from other Blender MCP servers. Most Blender MCP projects (e.g. ahujasid/blender-mcp, the official Blender Labs MCP server) drive a live, running Blender GUI over a TCP/add-on bridge for interactive scene editing — a different use case with a different trust model (an open socket, an installed add-on, a persistent process). This server instead targets CI-style, one-shot asset verification: run it in a pipeline step, get a pass/fail JSON, move on. If you need live GUI control, use a reviewed Blender MCP add-on separately (see Safety below).

[!NOTE] AI / LLM Integration & Machine-Readable Context: AI assistants (Claude, Codex, Gemini) can read llms.txt for machine-readable context, search phrases, and tool documentation. Regression test suites guard privacy hygiene and runtime memory safety.

[!TIP] CI & Asset Pipeline Automation: Use blender_verify_fbx_reimport as an automated gate before committing 3D assets to source control. It flags missing prefixes (e.g., SM_, M_), unexpected mesh counts, or broken material assignments without human intervention.

Architecture & Workflow

graph TD
    subgraph Client ["AI Assistant & Client Environment"]
        AI["AI Agent (Claude / Codex / Gemini)"]
        Config["MCP Configuration (npx / node)"]
    end

    subgraph Server ["ellmos Blender Use MCP Server"]
        MCP["MCP Protocol Server (src/index.js)"]
        subgraph Tools ["Tool Handlers"]
            T1["blender_verify_fbx_reimport"]
            T2["blender_run_script"]
            T3["blender_locate"]
        end
        Safety["Timeout & Tail Buffer Guard (8k chars)"]
    end

    subgraph Subprocess ["Headless Subprocess (Isolated)"]
        Exe["Blender Executable (blender --background)"]
        Python["Temp Python Verification Script"]
        FBX["Target FBX Asset File"]
        JSONOut["Deterministic JSON Result"]
    end

    AI -->|JSON-RPC Request| MCP
    MCP --> Tools
    T1 -->|Generates script & spawns| Exe
    T2 -->|Executes arbitrary python| Exe
    T3 -->|Locates binary| Exe
    Exe --> Python
    Python --> FBX
    FBX -->|Mesh / Material / Naming QA| JSONOut
    JSONOut --> Safety
    Safety -->|Bounded Response| AI

    style Client fill:#1e1e2e,stroke:#89b4fa,stroke-width:1px
    style Server fill:#181825,stroke:#cba6f7,stroke-width:1px
    style Subprocess fill:#11111b,stroke:#a6e3a1,stroke-width:1px

Tools

Tool	Purpose
blender_verify_fbx_reimport	Generate a temporary Blender verification script, import an FBX, and write a JSON result with mesh/material counts and missing required prefixes.
blender_run_script	Run blender --background --python <script.py> with optional arguments and bounded stdout tail.
blender_locate	Resolve the Blender executable from an explicit path, BLENDER_EXE, the verified local default, or PATH.

Safety

• This server runs local Python inside Blender. Use only scripts and asset paths you trust.
• The default timeout is bounded.
• No remote asset marketplaces, API keys, or telemetry are included.
• For live GUI control, use a reviewed Blender MCP add-on separately.

Installation

Option 1: Run via npx (no install)

{
  "mcpServers": {
    "blender-use": {
      "command": "npx",
      "args": ["-y", "ellmos-blender-use-mcp"]
    }
  }
}

Option 2: Install from source

git clone https://github.com/ellmos-ai/ellmos-blender-use-mcp.git
cd ellmos-blender-use-mcp
npm install
npm run build
node src/index.js

For a local checkout, point command/args at the cloned src/index.js instead:

{
  "mcpServers": {
    "blender-use": {
      "command": "node",
      "args": ["<path-to-repo>/src/index.js"]
    }
  }
}

Configuration

• BLENDER_EXE — optional path to the Blender executable. Without it, tools try the explicit blenderPath argument, then BLENDER_EXE, then a verified local Windows default, then PATH.
• Every tool also accepts an explicit blenderPath argument per call, which takes priority over BLENDER_EXE.
• Process output is retained only as a tail: blender_run_script defaults to 8,000 characters (configurable up to 50,000); FBX verification keeps 8,000. The response marks outputTruncated: true when earlier output was discarded, so verbose Blender scripts cannot grow the MCP process memory without bound.

License

MIT — see LICENSE.

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ellmos-ai Ecosystem

This MCP server is part of the ellmos-ai ecosystem — AI infrastructure, MCP servers, and intelligent tools.

MCP Server Family

Server	Tools	Focus	npm
FileCommander	46	Filesystem, process management, interactive sessions, cloud-lock-safe operations	ellmos-filecommander-mcp
CodeCommander	22	Code analysis, JSON repair, imports, diffs, regex	ellmos-codecommander-mcp
Clatcher	12	File repair, format conversion, batch operations	ellmos-clatcher-mcp
n8n Manager	18	n8n workflow management via AI assistants	n8n-manager-mcp
ControlCenter	20	MCP stack discovery, profile management, control plane	ellmos-controlcenter-mcp
Homebase	45	Local-first LLM memory, knowledge, state, routing, swarm orchestration	ellmos-homebase-mcp (alpha)
ServerCommander	8	Server operations: health checks, log analysis, deploy dry-runs, mail diagnostics	ellmos-servercommander-mcp (alpha)
Blender Use	3	Headless Blender asset QA and FBX reimport verification	ellmos-blender-use-mcp (alpha)
Open Compute	10	Model-agnostic computer use: capture, safety-gated actions, Windows UIA	open-compute-mcp (alpha)

AI Infrastructure

Project	Description
BACH	Local-first text-based OS for LLM agents — 113+ handlers, 550+ tools, SQLite memory
open-compute	Model-agnostic computer-use core powering Open Compute MCP
clutch	Provider-neutral LLM orchestration with auto-routing and budget tracking
rinnsal	Lightweight agent memory, connectors, and automation infrastructure
ellmos-stack	Self-hosted AI research stack (Ollama + n8n + Rinnsal + KnowledgeDigest)
MarbleRun	Autonomous agent chain framework for Claude Code
gardener	Minimalist database-driven LLM OS prototype (4 functions, 1 table)
ellmos-tests	Testing framework for LLM operating systems (7 dimensions)

Desktop Software

Our partner organization open-bricks bundles AI-native desktop applications — a modern, open-source software suite built for the age of AI. Categories include file management, document tools, developer utilities, and more.