BlenderMCP MCP Server

What does “BlenderMCP” MCP Server do?

BlenderMCP is an MCP (Model Context Protocol) server that bridges the gap between Blender, the popular open-source 3D creation suite, and AI assistants like Claude AI. By leveraging the MCP, BlenderMCP allows AI models to directly interact with and control Blender, enabling automated and prompt-assisted 3D modeling, real-time scene creation, and manipulation. This integration empowers developers and creators to streamline workflows, such as generating, modifying, or deleting 3D objects and assets within Blender through natural language prompts or LLM-driven commands. BlenderMCP enhances development productivity by automating repetitive Blender tasks, facilitating rapid prototyping, and enabling intelligent asset management, all while maintaining two-way communication between the AI and Blender.

List of Prompts

• No prompt templates were explicitly listed in the available documentation or files for BlenderMCP.

List of Resources

• No explicit MCP resource definitions are provided in the available documentation or repository files for BlenderMCP.

List of Tools

• Object Manipulation: Enables the creation, modification, and deletion of 3D objects within Blender through AI-triggered commands.
• Screenshot/Viewport Capture: Allows capturing screenshots of the Blender viewport for scene understanding (as described in release notes).
• Sketchfab Model Search/Download: Integrates with Sketchfab API to search and download 3D models directly into Blender.
• Poly Haven Asset Integration: Supports fetching assets from Poly Haven API and importing them into Blender.
• 3D Model Generation (Hyper3D Rodin): Enables generation of 3D models using the Hyper3D Rodin interface.

Use Cases of this MCP Server

• Prompt-Assisted 3D Modeling: AI can generate complex 3D models in Blender based on user prompts, reducing manual modeling effort.
• Automated Scene Creation: Instantly set up and populate Blender scenes by describing them to an AI, accelerating the creative process.
• Asset Search and Import: Search, download, and import assets from Sketchfab or Poly Haven directly through AI commands.
• Viewport Analysis & Feedback: Capture Blender viewport screenshots for AI-driven scene analysis, feedback, or documentation.
• Model Generation: Automatically generate new 3D models using generative tools like Hyper3D Rodin, guided by AI.

How to set it up

Windsurf

1. Ensure prerequisites are met (e.g., Python, Blender installed).
2. Open the Windsurf configuration file.
3. Add BlenderMCP as an MCP server using the JSON snippet below.
4. Save configuration and restart Windsurf.
5. Verify BlenderMCP connection in the MCP servers panel.

{
  "mcpServers": {
    "blender-mcp": {
      "command": "python",
      "args": ["-m", "blender_mcp"]
    }
  }
}

Claude

1. Install Blender and Python if not already installed.
2. Edit Claude’s MCP server configuration.
3. Add BlenderMCP with the provided command and arguments.
4. Restart Claude or reload configuration.
5. Check for successful integration in Claude’s MCP section.

{
  "mcpServers": {
    "blender-mcp": {
      "command": "python",
      "args": ["-m", "blender_mcp"]
    }
  }
}

Cursor

1. Install Blender and set up Python environment.
2. Locate the Cursor MCP configuration file.
3. Insert BlenderMCP server details as shown.
4. Save changes and restart Cursor.
5. Confirm BlenderMCP is listed among active MCP servers.

{
  "mcpServers": {
    "blender-mcp": {
      "command": "python",
      "args": ["-m", "blender_mcp"]
    }
  }
}

Cline

1. Ensure Blender and Python are properly installed.
2. Open the Cline MCP configuration file for editing.
3. Add BlenderMCP server configuration.
4. Save the file and restart Cline.
5. Validate that BlenderMCP is connected.

{
  "mcpServers": {
    "blender-mcp": {
      "command": "python",
      "args": ["-m", "blender_mcp"]
    }
  }
}

Securing API Keys

To securely use API keys (e.g., for Sketchfab or Poly Haven), store them in environment variables and reference them in your configuration:

{
  "mcpServers": {
    "blender-mcp": {
      "command": "python",
      "args": ["-m", "blender_mcp"],
      "env": {
        "SKETCHFAB_API_KEY": "${SKETCHFAB_API_KEY}"
      }
    }
  }
}

You can also add user inputs if supported by your platform:

{
  "inputs": {
    "sketchfab_api_key": {
      "type": "env",
      "env": "SKETCHFAB_API_KEY"
    }
  }
}

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "blender-mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “blender-mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Based on the above tables, BlenderMCP is a useful and popular MCP server for Blender-AI integration, with great practical tooling and broad adoption. However, documentation for prompts, resources, and advanced MCP features is lacking. Overall, I would rate this MCP a 7/10 for its utility and popularity, but with room for improvement in documentation and feature transparency.

MCP Score