BlenderMCP Pro

A secure MCP (Model Context Protocol) server for AI-driven 3D modeling in Blender. Connects any MCP-compatible AI client (Claude, etc.) to Blender through a safe, validated tool interface.

Architecture

AI Client (Claude, etc.)
    |
    | MCP Protocol (stdio)
    v
server/          -- FastMCP server with async TCP client
    |
    | TCP (127.0.0.1:9876, JSON-RPC 2.0, length-prefixed)
    v
addon/           -- Blender addon with TCP server & dispatcher
    |
    v
Blender Python API (bpy)

Key design principle: Zero exec()/eval() -- every operation maps to a static handler function with per-parameter input validation. No arbitrary code execution.

Tools (37)

Category	Count	Tools
Scene	7	get_info, list_objects, get_object, get_hierarchy, set_unit_system, manage_collection, set_active_object
Mesh	9	create_primitive, create_custom, edit_geometry, get_geometry, set_smooth_shading, separate, join, set_origin, analyze_profile
Object	8	set_transform, get_transform, duplicate, delete, parent, unparent, move_to_collection, apply_transform
Material	4	create, assign, get_info, update
Modifier	3	add, configure, apply
Viewport	5	screenshot, set_camera, auto_screenshot, screenshot_cleanup
Selection	1	set

Notable Tools

• mesh_edit_geometry -- Chained edit-mode operations (extrude, bevel, loop cut, scale, etc.) with smart selection (by position, normal, Z-range)
• mesh_analyze_profile -- Deep mesh analysis for reverse-engineering construction (Z-slice profiles, symmetry detection, material zones, loose parts)
• mesh_create_custom -- Create meshes from raw vertex/face data
• auto_screenshot -- Automatic viewport captures after every modifying operation

Setup

1. Install the Blender Addon

# Build the addon zip
python build_addon.py

# Or install directly (finds Blender automatically)
python install_addon.py

Then enable "BlenderMCP Pro" in Blender Preferences > Add-ons.

2. Install the MCP Server

pip install -e .

3. Configure Your AI Client

Add to your MCP client config (e.g. claude_desktop_config.json):

{
  "mcpServers": {
    "blendermcp": {
      "command": "blendermcp"
    }
  }
}

See claude_desktop_config_example.json for a full example.

4. Connect

1. Open Blender
2. In the addon panel, click "Start Server"
3. Start your MCP client
4. The AI can now control Blender

Running Tests

pip install -e ".[dev]"
pytest tests/ -v

Project Structure

addon/              Blender addon (TCP server, handlers, UI)
  handlers/         One module per tool category
  connection.py     TCP server & message framing
  dispatcher.py     Method routing
  validation.py     Input validation
server/             MCP server (FastMCP, async TCP client)
  tools/            One module per tool category
  blender_client.py Async TCP client
  auto_screenshot.py Screenshot management
shared/             Wire protocol, constants, error codes
tests/              pytest unit tests
training/           Training data tools (bulk analyzer, generators)

Training Data Tools

The training/ directory contains tools for generating and analyzing 3D modeling data:

• bulk_analyze.py -- Headless Blender script to batch-analyze FBX/OBJ/GLB files into JSON profiles
• generate_training_data.py -- Procedural generator for MCP tool-call training sequences (38 object categories, no LLM needed)
• fbx_to_recipe.py -- Converts 3D files into exact mesh_create_custom reconstruction recipes

# Analyze a directory of FBX files
blender --background --python training/bulk_analyze.py -- --input ./models/ --output ./templates/

# Generate 10K training samples
python training/generate_training_data.py --count 10000 --format chatml --split --output ./generated/

License

MIT