Blender MCP Server

Blender MCP Server

Enables AI agents to control Blender 3D software through natural language commands, supporting object creation, manipulation, materials, rendering, and scene management with 22 tools organized across 6 categories.

Category
访问服务器

README

View the Demo Here!

https://drive.google.com/file/d/1VHnKps0HPqw4ipIw1GG_X68u8iuSRbCK/view

Prerequisites

Before you begin, make sure you have:

  • Python 3.13+ installed
  • Blender 5.0+ installed at /Applications/Blender.app/Contents/MacOS/Blender (macOS)
    • For Linux/Windows: Update the blender_path in blender_mcp_filter.py
  • Claude Desktop installed (for AI agent integration)

How to Install Dependencies

cd /path/to/blender_takehome
pip install -e .

This installs:

  • fastmcp>=2.12.4 - MCP server framework
  • pydantic>=2.0.0 - Input validation
  • fake-bpy-module-latest - Type stubs for development

How to Run the Server

This is the easiest way to use the server with Claude Desktop:

  1. Copy the configuration file:

    cp claude_desktop_config.json ~/Library/Application\ Support/Claude/claude_desktop_config.json
    
  2. Edit the configuration file and update the paths to match your project location:

    {
      "mcpServers": {
        "blender-server": {
          "command": "python3",
          "args": [
            "/YOUR/PATH/TO/blender_takehome/blender_mcp_filter.py"
          ],
          "env": {
            "PYTHONPATH": "/YOUR/PATH/TO/blender_takehome:/YOUR/PATH/TO/blender_takehome/src"
          }
        }
      }
    }
    
  3. Now Open Blender -- the Blender MCP server will automatically be running now.

  4. Restart Claude Desktop - it will automatically launch the Blender MCP server when you start a conversation.

  5. Start Experimenting: Ask Claude to create a cube in Blender. You should see Blender open and a cube appear!

List of Tools Implemented

The server provides 22 tools organized into the following categories:

Object Creation (5 tools)

  • create_cube_tool - Create a cube primitive
  • create_sphere_tool - Create a UV sphere primitive
  • create_cylinder_tool - Create a cylinder primitive
  • create_plane_tool - Create a plane primitive
  • duplicate_object_tool - Duplicate an existing object

Object Manipulation (5 tools)

  • move_object_tool - Move an object to a new location
  • rotate_object_tool - Rotate an object
  • scale_object_tool - Scale an object
  • delete_object_tool - Delete an object from the scene
  • select_object_tool - Select an object in the scene

Scene Management (3 tools)

  • list_objects_tool - List all objects in the scene
  • get_object_info_tool - Get detailed information about an object
  • clear_scene_tool - Remove all objects from the scene

Camera Operations (1 tool)

  • set_active_camera_tool - Set the active camera for rendering

Materials (2 tools)

  • create_material_tool - Create a new material with a base color
  • assign_material_tool - Assign a material to an object

Rendering (3 tools)

  • create_camera_tool - Create and configure a camera
  • create_light_tool - Create a light source
  • render_scene_tool - Render the scene to an image file

File Operations (3 tools)

  • get_scene_filepath_tool - Get the current Blender file path
  • save_file_tool - Save the scene to a file
  • open_file_tool - Open an existing Blender file

Usage Examples

Once connected to Claude Desktop, you can ask Claude to:

  • "Create a red cube at position (2, 0, 0)"
  • "Add a sphere with radius 1.5"
  • "Create a material called 'Metal' with color (0.8, 0.8, 0.9)"
  • "Render the scene to /path/to/output.png"
  • "List all objects in the scene"
  • "Save the current file as "_______/Project.blend"

Claude will use the MCP tools to execute these commands in Blender.

Project Structure

blender_takehome/
├── src/
│   ├── models.py      # Pydantic input validation models
│   ├── operations.py   # Pure Blender operations (bpy API)
│   ├── tools.py       # MCP tool wrappers
│   └── server.py      # FastMCP server setup
├── blender_mcp_filter.py    # Launches Blender and filters stdout
├── blender_mcp_server.py   # Entry point script for Blender
├── claude_desktop_config.json  # Claude Desktop configuration
└── pyproject.toml      # Python dependencies

Design Choices

The server follows a three-layer architecture:

  1. Models (src/models.py) - Pydantic models validate all inputs
  2. Operations (src/operations.py) - Pure functions that interact with Blender's bpy API
  3. Tools (src/tools.py) - MCP tool wrappers that expose operations to AI agents

I decided to separate this project into this three-layer architecture in order to isolate where errors were occuring very easily. This helped a lot in the debugging process. This has also simplified the creation of adding new tools within the MCP arsenal.

All that needs to be done to add a new tale is:

  1. Add model in src/models.py:

    class MyToolInput(BaseModel):
        param: str = Field(...)
    
  2. Add operation in src/operations.py:

    def my_operation(input: MyToolInput) -> str:
        # Blender code here
        return "Success: ..."
    
  3. Add tool in src/tools.py:

    @mcp.tool()
    async def my_tool_tool(param: str) -> str:
        input_model = MyToolInput(param=param)
        return my_operation(input_model)
    

That's it! FastMCP automatically registers the tool.

Tool Call Flow

1. Claude Desktop sends JSON-RPC request:
   {"method": "tools/call", "params": {"name": "create_cube_tool", "arguments": {...}}}

2. FastMCP receives request, routes to create_cube_tool()

3. tools.py: create_cube_tool() validates input with CreateCubeInput

4. operations.py: create_cube() executes bpy.ops.mesh.primitive_cube_add()

5. Blender creates cube, updates scene

6. operations.py: Returns success message string

7. tools.py: Returns string to FastMCP

8. FastMCP sends JSON-RPC response:
   {"result": {"content": [{"type": "text", "text": "Successfully created cube..."}]}}

9. Claude Desktop receives response

Error Flow

1. Invalid input (e.g., size = -1.0)

2. Pydantic validation fails in models.py

3. ValidationError raised with clear message

4. tools.py catches exception, returns "Error: size must be > 0.001"

5. FastMCP sends error response to Claude Desktop

6. Server continues running (no crash)

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选