Fusion 360 MCP - Multi-Model AI Integration

FusionMCP is a comprehensive Model Context Protocol (MCP) integration layer that connects Autodesk Fusion 360 with multiple AI backends (Ollama, OpenAI, Google Gemini, and Anthropic Claude) to enable AI-powered parametric CAD design through natural language.

Version Python Fusion 360 License

🎯 Features

🤖 Multi-Model Support: Seamlessly switch between Ollama, OpenAI GPT-4o, Google Gemini, and Claude 3.5
🔄 Intelligent Routing: Automatic fallback chain when primary model fails
📐 Parametric Design: AI understands and generates parametric CAD operations
🛡️ Safety First: Built-in validation for dimensions, units, and geometric feasibility
💾 Context Caching: Conversation and design state persistence (JSON/SQLite)
🎨 Fusion 360 Integration: Native add-in for seamless workflow
⚡ Async Architecture: Fast, non-blocking operations with retry logic
📊 Structured Logging: Detailed logs with Loguru

📋 Table of Contents

🏗️ Architecture

┌─────────────────────────────────────────────────────────────┐ │ Fusion 360 User │ │ ↓ │ │ ┌─────────────────────────┐ │ │ │ Fusion 360 Add-in │ │ │ │ - UI Dialog │ │ │ │ - Action Executor │ │ │ │ - Network Client │ │ │ └──────────┬──────────────┘ │ │ ↓ HTTP/REST │ │ ┌─────────────────────────┐ │ │ │ MCP Server (FastAPI) │ │ │ │ - Router │ │ │ │ - Schema Validation │ │ │ │ - Context Cache │ │ │ └──────────┬──────────────┘ │ │ ↓ │ │ ┌───────────────┴───────────────────┐ │ │ ↓ ↓ ↓ ↓ │ │ ┌─────────┐ ┌──────────┐ ┌────────┐ ┌──────────┐ │ │ │ Ollama │ │ OpenAI │ │ Gemini │ │ Claude │ │ │ │ (Local) │ │ API │ │ API │ │ API │ │ │ └─────────┘ └──────────┘ └────────┘ └──────────┘ │ │ │ │ System Prompt (FusionMCP Personality) │ │ ↓ │ │ Structured JSON Actions → Fusion 360 │ └─────────────────────────────────────────────────────────────┘

Component Overview

Fusion 360 Add-in (fusion_addin/)
- Python-based Fusion 360 add-in
- Captures user intent and design context
- Executes structured CAD actions
- Real-time UI feedback
MCP Server (mcp_server/)
- FastAPI-based REST server
- Routes requests to appropriate LLM
- Validates and normalizes responses
- Caches conversation history
LLM Clients (mcp_server/llm_clients/)
- Unified interface for all models
- Provider-specific implementations
- Automatic retry and error handling
System Prompt (prompts/system_prompt.md)
- Defines FusionMCP personality
- Enforces JSON output format
- Provides action schema templates

🚀 Installation

Prerequisites

Python 3.11+ (for MCP server)
Autodesk Fusion 360 (2025 version recommended)
At least one LLM provider:
- Ollama (local, free)
- OpenAI API Key
- Google AI API Key
- Anthropic API Key

Step 1: Clone Repository

git clone https://github.com/yourusername/fusion360-mcp.git cd fusion360-mcp

Step 2: Install Python Dependencies

# Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dependencies pip install -r requirements.txt # Or install in development mode pip install -e .

Step 3: Configure Environment

Create config.json from example:

cp examples/example_config.json config.json

Edit config.json with your API keys:

{ "ollama_url": "http://localhost:11434", "openai_api_key": "sk-proj-...", "gemini_api_key": "AIza...", "claude_api_key": "sk-ant-...", "default_model": "openai:gpt-4o-mini", "mcp_host": "127.0.0.1", "mcp_port": 9000 }

Alternative: Use environment variables (.env file):

OPENAI_API_KEY=sk-proj-... GEMINI_API_KEY=AIza... CLAUDE_API_KEY=sk-ant-...

Step 4: Install Fusion 360 Add-in

Copy fusion_addin/ folder to Fusion 360 add-ins directory:
- Windows: %APPDATA%\Autodesk\Autodesk Fusion 360\API\AddIns\
- macOS: ~/Library/Application Support/Autodesk/Autodesk Fusion 360/API/AddIns/
Rename to FusionMCP:
cp -r fusion_addin "/Users/YOUR_USER/Library/Application Support/Autodesk/Autodesk Fusion 360/API/AddIns/FusionMCP"
Restart Fusion 360
Open Fusion 360 → Scripts and Add-Ins → Add-Ins tab → Select FusionMCP → Run

🎬 Quick Start

1. Start MCP Server

# Activate virtual environment source venv/bin/activate # Start server python -m mcp_server.server

Expected output:

2. Test Server (Optional)

curl -X POST http://127.0.0.1:9000/mcp/command \ -H "Content-Type: application/json" \ -d '{ "command": "ask_model", "params": { "provider": "openai", "model": "gpt-4o-mini", "prompt": "Create a 20mm cube" }, "context": { "active_component": "RootComponent", "units": "mm", "design_state": "empty" } }'

3. Use in Fusion 360

Open Fusion 360
Click Scripts and Add-Ins → Add-Ins → FusionMCP → Run
Click MCP Assistant button in toolbar
Enter natural language command:
- "Create a 20mm cube"
- "Design a mounting bracket with 4 holes"
- "Make a cylindrical shaft 10mm diameter, 50mm long"

⚙️ Configuration

Full Configuration Options

{ // API Configuration "ollama_url": "http://localhost:11434", "openai_api_key": "sk-proj-...", "gemini_api_key": "AIza...", "claude_api_key": "sk-ant-...", // Model Selection "default_model": "openai:gpt-4o-mini", "fallback_chain": [ "openai:gpt-4o-mini", "gemini:gemini-1.5-flash-latest", "ollama:llama3" ], // Server Settings "mcp_host": "127.0.0.1", "mcp_port": 9000, "allow_remote": false, // Logging "log_level": "INFO", "log_dir": "logs", // Caching "cache_enabled": true, "cache_type": "json", // or "sqlite" "cache_path": "context_cache.json", // Timeouts and Retries "timeout_seconds": 30, "max_retries": 3, "retry_delay": 1.0, // Available Models "models": { "ollama": { "available": ["llama3", "mistral", "codellama"], "default": "llama3" }, "openai": { "available": ["gpt-4o", "gpt-4o-mini", "gpt-4-turbo"], "default": "gpt-4o-mini" }, "gemini": { "available": ["gemini-1.5-pro-latest", "gemini-1.5-flash-latest"], "default": "gemini-1.5-flash-latest" }, "claude": { "available": ["claude-3-5-sonnet-20241022"], "default": "claude-3-5-sonnet-20241022" } } }

💡 Usage Examples

Example 1: Simple Geometry

Prompt: "Create a 20mm cube"

Generated Action:

{ "action": "create_box", "params": { "width": 20, "height": 20, "depth": 20, "unit": "mm" }, "explanation": "Creating a 20mm cubic box", "safety_checks": ["dimensions_positive", "units_valid"] }

Example 2: Complex Design

Prompt: "Design a mounting bracket 100x50mm with 4 M5 mounting holes"

Generated Action Sequence:

{ "actions": [ { "action": "create_box", "params": {"width": 100, "height": 50, "depth": 5, "unit": "mm"}, "explanation": "Create base plate" }, { "action": "create_hole", "params": {"diameter": 5.5, "position": {"x": 10, "y": 10}, "unit": "mm"}, "explanation": "M5 clearance hole (10mm edge offset)" }, // ... 3 more holes ], "total_steps": 5 }

Example 3: Parametric Design

Prompt: "Create a shaft with diameter 2x of length"

{ "clarifying_questions": [ { "question": "What is the shaft length?", "context": "Need length to calculate diameter (diameter = 2 × length)", "suggestions": ["50mm", "100mm", "Custom"] } ] }

📡 API Reference

Endpoints

POST `/mcp/command`

Execute MCP command.

Request Body:

{ "command": "ask_model", "params": { "provider": "openai", "model": "gpt-4o-mini", "prompt": "User prompt here", "temperature": 0.7, "max_tokens": 2000 }, "context": { "active_component": "RootComponent", "units": "mm", "design_state": "empty" } }

Response:

{ "status": "success", "message": "Action generated successfully", "actions_to_execute": [...], "llm_response": {...} }

GET `/health`

Health check.

Response:

{ "status": "healthy", "providers": ["ollama", "openai", "gemini", "claude"], "cache_enabled": true }

GET `/models`

List available models.

Response:

{ "models": { "ollama": ["llama3", "mistral"], "openai": ["gpt-4o", "gpt-4o-mini"], "gemini": ["gemini-1.5-pro-latest"], "claude": ["claude-3-5-sonnet-20241022"] } }

GET `/history?limit=10`

Get conversation history.

Response:

{ "conversations": [...], "actions": [...] }

Supported Actions

Action	Description	Required Params
`create_box`	Create rectangular box	`width` , `height` , `depth` , `unit`
`create_cylinder`	Create cylinder	`radius` , `height` , `unit`
`create_sphere`	Create sphere	`radius` , `unit`
`create_hole`	Create hole	`diameter` , `position` , `unit`
`extrude`	Extrude profile	`profile` , `distance` , `unit`
`fillet`	Round edges	`edges` , `radius` , `unit`
`apply_material`	Apply material	`material_name`

🔬 Model Comparison

Feature	Ollama (Local)	OpenAI GPT-4o	Google Gemini	Claude 3.5
Cost	Free	$$	$	$$$
Speed	Fast	Medium	Fast	Medium
Offline	✅ Yes	❌ No	❌ No	❌ No
JSON Mode	Limited	✅ Native	Good	Good
Reasoning	Good	Excellent	Very Good	Excellent
Geometry	Good	Very Good	Excellent	Very Good
Creative	Good	Excellent	Very Good	Good
Best For	Privacy, Offline	Creative designs	Spatial reasoning	Safety validation

Recommended Workflows

Creative Design: OpenAI GPT-4o → Claude (validation)
Geometric Precision: Gemini → OpenAI
Privacy-First: Ollama (all tasks)
Cost-Optimized: Gemini Flash → Ollama (fallback)

🛠️ Development

Project Structure

fusion360-mcp/ ├── mcp_server/ # MCP Server │ ├── server.py # FastAPI app │ ├── router.py # Request routing │ ├── schema/ # Pydantic models │ ├── llm_clients/ # LLM implementations │ └── utils/ # Utilities ├── fusion_addin/ # Fusion 360 Add-in │ ├── main.py # Entry point │ ├── ui_dialog.py # UI components │ ├── fusion_actions.py # Action executor │ └── utils/network.py # Network client ├── prompts/ # System prompts ├── examples/ # Example configs ├── tests/ # Test suite ├── requirements.txt # Dependencies └── README.md # This file

Running Tests

# Run all tests pytest tests/ -v # Run specific test file pytest tests/test_mcp_server.py -v # Run with coverage pytest tests/ --cov=mcp_server --cov-report=html

Adding New LLM Provider

Create client in mcp_server/llm_clients/new_provider_client.py:

class NewProviderClient: async def generate(self, model, prompt, system_prompt, temperature, max_tokens): # Implementation return { "provider": "new_provider", "model": model, "output": "...", "json": {...}, "tokens_used": 123 }

if config.new_provider_api_key: self.clients["new_provider"] = NewProviderClient(...)

Code Style

PEP8 compliant
Type annotations required
Docstrings for all functions/classes
Async/await for I/O operations

🐛 Troubleshooting

Common Issues

1. Server Won't Start

Error: Address already in use

Solution: Change port in config.json:

{"mcp_port": 9001}

2. Fusion Add-in Not Visible

Solution:

Verify add-in is in correct folder
Check FusionMCP.manifest exists
Restart Fusion 360
Check Scripts and Add-Ins → Add-Ins tab

3. API Key Errors

Error: 401 Unauthorized

Solution:

Verify API key in config.json
Check key has proper permissions
Try environment variables instead

4. Ollama Connection Failed

Error: Connection refused

Solution:

# Check Ollama is running ollama list # Start Ollama service ollama serve

5. JSON Parsing Errors

Solution:

Check system prompt is loaded
Verify model supports JSON mode
Use temperature < 0.8 for better structure
Enable json_mode=True in OpenAI client

Debug Mode

Enable verbose logging:

{"log_level": "DEBUG"}

Check logs in logs/mcp_server.log

Health Check

# Check server health curl http://127.0.0.1:9000/health # List available models curl http://127.0.0.1:9000/models # View conversation history curl http://127.0.0.1:9000/history?limit=5

🧪 Testing the System

Manual CLI Test

curl -X POST http://127.0.0.1:9000/mcp/command \ -H "Content-Type: application/json" \ -d @examples/example_command.json

Python Test Script

import requests command = { "command": "ask_model", "params": { "provider": "openai", "model": "gpt-4o-mini", "prompt": "Create a 10mm cube" }, "context": { "units": "mm", "design_state": "empty" } } response = requests.post("http://127.0.0.1:9000/mcp/command", json=command) print(response.json())

🤝 Contributing

Contributions welcome! Please:

Fork the repository
Create feature branch (git checkout -b feature/amazing-feature)
Commit changes (git commit -m 'Add amazing feature')
Push to branch (git push origin feature/amazing-feature)
Open Pull Request

Development Setup

# Install dev dependencies pip install -e ".[dev]" # Install pre-commit hooks pre-commit install # Run linting ruff check mcp_server/ black mcp_server/

📄 License

MIT License - see LICENSE file

🙏 Acknowledgments

Autodesk Fusion 360 API
FastAPI framework
Anthropic, OpenAI, Google for LLM APIs
Ollama for local LLM support

📞 Support

Issues: GitHub Issues
Discussions: GitHub Discussions
Documentation: Wiki

🗺️ Roadmap

WebSocket streaming for real-time chat
Vision model support (CAD screenshot analysis)
Multi-agent orchestration
Generative Design API integration
Geometry export to Markdown/docs
Fusion 360 UI palette integration
3D preview before execution
Undo/redo action history
Cloud deployment support

Built with ❤️ for the Fusion 360 and AI community

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Enables AI-powered parametric CAD design in Autodesk Fusion 360 through natural language commands. Supports multiple AI backends (Ollama, OpenAI, Gemini, Claude) with intelligent routing and safety validation for geometric operations.

Fusion 360 MCP - Multi-Model AI Integration

🎯 Features

📋 Table of Contents

🏗️ Architecture

Component Overview

🚀 Installation

Prerequisites

Step 1: Clone Repository

Step 2: Install Python Dependencies

Step 3: Configure Environment

Step 4: Install Fusion 360 Add-in

🎬 Quick Start

1. Start MCP Server

2. Test Server (Optional)

3. Use in Fusion 360

⚙️ Configuration

Full Configuration Options

💡 Usage Examples

Example 1: Simple Geometry

Example 2: Complex Design

Example 3: Parametric Design

📡 API Reference

Endpoints

POST /mcp/command

GET /health

GET /models

GET /history?limit=10

Supported Actions

🔬 Model Comparison

Recommended Workflows

🛠️ Development

Project Structure

Running Tests

Adding New LLM Provider

Code Style

🐛 Troubleshooting

Common Issues

1. Server Won't Start

2. Fusion Add-in Not Visible

3. API Key Errors

4. Ollama Connection Failed

5. JSON Parsing Errors

Debug Mode

Health Check

🧪 Testing the System

Manual CLI Test

Python Test Script

🤝 Contributing

Development Setup

📄 License

🙏 Acknowledgments

📞 Support

🗺️ Roadmap

New MCP Servers

MCP directory API

POST `/mcp/command`

GET `/health`

GET `/models`

GET `/history?limit=10`