🥗 USDA Nutrition MCP Enabled Server

Professional Model Context Protocol (MCP) enabled server for USDA FoodData Central
Transforms 600k+ foods into intelligent nutrition tools for Claude Desktop and other MCP clients

🌟 What This Demonstrates

This project showcases professional MCP implementation skills:

✅ Dual Architecture - Both MCP protocol server AND HTTP API
✅ Production Bridge - Smart mcp_bridge.py with hosted/local/custom server support
✅ Three Deployment Options - Hosted service, local development, custom server
✅ Type-Safe Models - Pydantic schemas with proper validation
✅ Docker + Cloud Run - Complete deployment pipeline

🚀 Quick Start for Claude Desktop

Option 1: Minimal Installation (Recommended)

Download just the bridge file - no need to clone the entire repository:

# Download the bridge
wget https://raw.githubusercontent.com/zen-apps/mcp-nutrition-tools/main/src/mcp_bridge.py

# Install dependencies
pip install mcp httpx

Then add to your Claude Desktop config:

{
  "mcpServers": {
    "usda-nutrition": {
      "command": "python3",
      "args": ["/path/to/downloaded/mcp_bridge.py"]
    }
  }
}

Mac Users with Virtual Environment

# Navigate to your project
cd /Users/yourusername/your-project-folder

# Create new venv in the project folder
python3 -m venv venv

# Activate it
source venv/bin/activate

# Install dependencies
pip install mcp httpx

# Test it works
python src/mcp_bridge.py --server-url https://usda-nutrition-mcp-356272800218.us-central1.run.app

Option 2: Full Repository (For Development)

{
  "mcpServers": {
    "usda-nutrition": {
      "command": "python3",
      "args": ["/path/to/mcp-nutrition-tools/src/mcp_bridge.py"],
      "cwd": "/path/to/mcp-nutrition-tools"
    }
  }
}

Option 2: Local Development

{
  "mcpServers": {
    "usda-nutrition": {
      "command": "python3",
      "args": [
        "/path/to/mcp-nutrition-tools/src/mcp_bridge.py",
        "--server-url",
        "http://localhost:8080"
      ],
      "cwd": "/path/to/mcp-nutrition-tools"
    }
  }
}

Option 3: Custom Server

{
  "mcpServers": {
    "usda-nutrition": {
      "command": "python3", 
      "args": [
        "/path/to/mcp-nutrition-tools/src/mcp_bridge.py",
        "--server-url",
        "https://your-server.com"
      ],
      "cwd": "/path/to/mcp-nutrition-tools"
    }
  }
}

See examples/configs/claude_desktop_config_examples.json for detailed configuration examples.

🔧 For Non-Claude Desktop Users

Direct HTTP API

Live API: https://usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app
Documentation: https://usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app/docs

# Search foods
curl -X POST "https://usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app/tools/search_foods" \
  -H "Content-Type: application/json" \
  -d '{"query": "chicken breast", "page_size": 5}'

# Get nutrition details  
curl -X POST "https://usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app/tools/get_food_nutrition" \
  -H "Content-Type: application/json" \
  -d '{"fdc_id": 171688}'

See API_USAGE.md for complete integration examples with Python, JavaScript, LangChain, and OpenAI.

🛠 MCP Tools Available

Once configured, Claude Desktop gets these nutrition tools:

• search_foods - Search USDA database by text
• get_food_nutrition - Get detailed nutrition for specific food
• compare_foods - Compare nutrition between multiple foods

Example Claude Interaction

You: "Compare the protein content of chicken breast vs salmon"

Claude: Uses MCP tools automatically:

1. search_foods("chicken breast") → Finds FDC ID 171077
2. search_foods("salmon") → Finds FDC ID 175167
3. compare_foods([171077, 175167]) → Gets comparison data
4. Provides detailed analysis with recommendations

🏗 Architecture Deep Dive

Dual Server Design

Claude Desktop ←→ mcp_bridge.py ←→ HTTP API ←→ USDA FoodData Central
     (MCP)              ↑              ↑              ↑
                   Smart Bridge    FastAPI       Rate Limited
                                                   Client

Key Implementation Details:

• src/mcp_server.py - FastMCP protocol server
• src/mcp_http_server.py - FastAPI HTTP server
• src/mcp_bridge.py - Smart bridge with server auto-detection
• src/usda_client.py - API client with retry logic
• src/models/ - Type-safe Pydantic schemas

Smart Bridge Logic

The bridge automatically detects server type and provides appropriate user feedback:

# Hosted service detection
if "usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app" in args.server_url:
    print("🌐 Using hosted service (1,000 requests/hour shared)")

# Local development  
elif "localhost" in args.server_url:
    print("🏠 Using local server (requires your USDA API key)")

📦 Installation & Development

# Clone and setup
git clone https://github.com/zen-apps/mcp-nutrition-tools
cd mcp-nutrition-tools
pip install -r requirements.txt

# Get USDA API key (for local development)
# Visit: https://fdc.nal.usda.gov/api-guide.html
echo "FDC_API_KEY=your_key_here" > .env

# Test MCP server
python -m src.mcp_server

# Test HTTP server  
python -m src.mcp_http_server

# Run tests
python -m pytest tests/ -v

# Code quality
ruff check src/
ruff format src/
mypy src/

🐳 Deployment Options

Local Development

# Run HTTP server locally
python -m src.mcp_http_server

# Run with Docker
make up

Production Deployment

# Deploy to Google Cloud Run
export FDC_API_KEY="your_usda_key"
./scripts/deploy-gcp.sh

The production deployment includes:

• Automatic SSL/HTTPS
• Health checks and monitoring
• Auto-scaling based on demand
• Structured logging

🔑 Configuration

Environment Variables

• FDC_API_KEY - USDA FoodData Central API key (required for local)
• ENVIRONMENT - "development" or "production"
• LOG_LEVEL - Logging level (DEBUG, INFO, etc.)

Rate Limits

• Hosted Service: 1,000 requests/hour (shared)
• Local Deployment: 1,000 requests/hour (your key)
• Enterprise: Contact for higher limits

🧪 Testing Strategy

# Quick connectivity test
python test_quick.py

# Full test suite with mocking
python -m pytest tests/ -v

# Test specific MCP tools
python examples/live_demo.py

The test suite includes:

• USDA API mocking with httpx-mock
• Async MCP server testing
• Integration test examples
• Performance benchmarking

🤝 Contributing

1. Fork the repository
2. Create feature branch: git checkout -b feature/amazing-feature
3. Run tests: python -m pytest tests/
4. Run linting: ruff check src/
5. Submit pull request

📄 License

MIT License - see LICENSE file for details.

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🎯 Ready to use? See examples/configs/claude_desktop_config_examples.json for setup instructions!

🔗 Live API: https://usda-nutrition-mcp-oc46l7ob5a-uc.a.run.app/docs