Install
$ agentstack add mcp-alwayssany-food-nutrition Open-source listing — not yet scanned by AgentStack. Follow the source repository for install instructions.
Security review
⚠ Flagged1 finding(s); flagged for manual review. · v0.1.0 How review works →
- • Prompt-injection patterns
- • Secret / credential exfiltration
- • Dangerous shell & filesystem operations
- • Untrusted network calls
- • Known-malicious package signatures
- high Pipes remote content directly into a shell (remote code execution).
What it can access
- ● Network access Used
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
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
- Install Python 3.11+ if not already installed.
- (Recommended) Create and activate a virtual environment:
``bash python3 -m venv .venv source .venv/bin/activate ``
- Install UV (optional, for fast dependency management):
``bash curl -LsSf https://astral.sh/uv/install.sh | sh ``
- Install project dependencies:
``bash 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 registrationsrc/tools/— Nutrition, meal planning, and dietary analysis toolssrc/services/— Integrations with USDA, Edamam, and other APIssrc/resources/— Nutrition databases and static resourcessrc/prompts/— AI prompt templatessrc/models/— Data models (Pydantic)src/utils/— Utilities and helpers
Contributing
Contributions are welcome! Please see the guidelines below:
- Fork the repository and create a new branch for your feature or fix.
- Install development dependencies:
``bash uv pip install .[dev] # Or pip install .[dev] ``
- Run tests:
``bash pytest ``
- Format code with Black and check typing with MyPy:
``bash black src/ mypy src/ ``
- 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](./technical_details.md). - For questions or support, open an issue or contact the maintainer listed in
pyproject.toml.
Source & license
This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: AlwaysSany
- Source: AlwaysSany/food-nutrition
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.