Food & Nutrition Intelligence MCP Server

A large MCP server project structure with food and nutrition intelligence, providing tools, resources, and prompts for nutrition data retrieval, meal planning, and dietary analysis.

Features

• Nutrition Data Tools: Retrieve detailed nutrition information from USDA FoodData Central and Edamam APIs
• Meal Planning: Generate meal plans based on dietary requirements and preferences
• Dietary Analysis: Analyze nutritional content of meals and diets
• Resource Endpoints: Access nutrition databases and food information
• AI Prompts: Pre-built prompt templates for nutrition-related AI interactions

Installation

1. Install Python 3.11+ if not already installed.
2. (Recommended) Create and activate a virtual environment:

   python3 -m venv .venv
   source .venv/bin/activate
   

3. Install UV (optional, for fast dependency management):

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

4. Install project dependencies:

   uv sync
   

Usage

To start the Food & Nutrition Intelligence MCP server:

uv run main.py

Or, if you have an entrypoint defined (e.g., via FastMCP CLI):

fastmcp run main.py

The server exposes a set of MCP tools for nutrition data, meal planning, and dietary analysis. You can interact with it via a compatible MCP client or by integrating it into your AI workflow.

Run on debug mode

npx @modelcontextprotocol/inspector uv run main.py

Example API Usage

• Get Nutrition Data:
  • Tool: nutrition_get_food_data(food_name: str, portion_size: float = 100.0, include_detailed: bool = False)
• Generate Meal Plan:
  • Tool: meal_plan_generate(dietary_preferences: dict, calories: int)
• Analyze Diet:
  • Tool: dietary_analysis(analyzed_meals: list)

See the technical details for more tool signatures and usage patterns.

Project Structure

• src/server.py — Main server entrypoint and tool registration
• src/tools/ — Nutrition, meal planning, and dietary analysis tools
• src/services/ — Integrations with USDA, Edamam, and other APIs
• src/resources/ — Nutrition databases and static resources
• src/prompts/ — AI prompt templates
• src/models/ — Data models (Pydantic)
• src/utils/ — Utilities and helpers

Contributing

Contributions are welcome! Please see the guidelines below:

1. Fork the repository and create a new branch for your feature or fix.
2. Install development dependencies:

   uv pip install .[dev]
   # Or
   pip install .[dev]
   

3. Run tests:

   pytest
   

4. Format code with Black and check typing with MyPy:

   black src/
   mypy src/
   

5. Submit a pull request describing your changes.

License

MIT License. See LICENSE file for details.

More Information

• For advanced architecture and technical details, see technical_details.md.
• For questions or support, open an issue or contact the maintainer listed in pyproject.toml.