blender-mcp-connector

name	blender-mcp-connector
description	MCP connector for Blender 3D creation suite – enables AI-powered 3D modeling, scene management, rendering, and animation through Claude
license	GPL
tags	["creative","3d-modeling","mcp","blender","animation"]
difficulty	expert
time_to_master	4-8 weeks
version	1.0.0

Blender MCP Connector

Overview

This skill enables Claude to interact with Blender through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Blender's Python API (bpy), allowing natural language control of Blender operations, intelligent automation, and AI-powered assistance for Blender workflows.

When to Use This Skill

• Scene creation and object manipulation via natural language
• Material and shader node graph generation
• Animation keyframe management
• Render settings optimization
• Python script generation for complex operations

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Blender          │
│   (Client)   │◀────│   (Python/TypeScript)  │◀────│   (Python API (bp)│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Blender operations as tools Claude can invoke. The server translates natural language intentions into Python API (bpy) calls.

Key Endpoints/Interfaces

bpy.ops.mesh.*, bpy.ops.object.*, bpy.data.materials, bpy.context.scene, bpy.ops.render.*

Implementation

// Blender MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "blender-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Blender resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Blender Python API (bpy)
    const response = await fetch(`${BASE_URL}bpy.ops.mesh.*`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Blender",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}bpy.ops.mesh.*`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Blender data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}bpy.ops.mesh.*`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "blender-mcp-connector": {
      "command": "node",
      "args": ["path/to/blender-mcp-connector/index.js"],
      "env": {
        "BLENDER_API_KEY": "your-api-key",
        "BLENDER_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Blender API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Blender API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Create a low-poly landscape scene with mountains, trees, and a river with PBR materials"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Blender Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

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