food-nutrition

AlwaysSany/food-nutrition

3.2

If you are the rightful owner of food-nutrition and would like to certify it and/or have it hosted online, please leave a comment on the right or send an email to henry@mcphub.com.

The Food & Nutrition Intelligence MCP Server is a comprehensive platform designed to provide tools, resources, and prompts for nutrition data retrieval, meal planning, and dietary analysis.

Tools

  1. nutrition_get_food_data

    Retrieve nutrition data for a specified food item.

  2. meal_plan_generate

    Generate a meal plan based on dietary preferences and calorie requirements.

  3. dietary_analysis

    Analyze the nutritional content of a list of meals.

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 .
  • For questions or support, open an issue or contact the maintainer listed in pyproject.toml.