USDA api
This server allow you to ask questions with way more accurate nutrition facts.
š USDA Food Tools for Claude
Add powerful food and nutrition data to Claude for Desktop using the USDA's comprehensive food database.
š For Users - Quick Install
Option 1: GUI Installer (Recommended)
- Download:
USDA Food Tools Installer.zip(11KB) - Extract and double-click the app
- Get API Key: Free at api.nal.usda.gov
- Follow the installer - opens in your browser
- Restart Claude - tools appear automatically!
Option 2: Command Line
curl -sSL https://raw.githubusercontent.com/yourusername/usda-api-mcp/main/install.sh | bash
š What You Get
- Search Foods - Find any food with detailed nutrition facts
- Nutrition Analysis - Complete nutrient breakdowns
- Brand Information - Ingredients, categories, manufacturers
- Bulk Lookups - Multiple foods at once
- Research Data - USDA's gold-standard food database
š Requirements
- Claude for Desktop (latest version)
- Free USDA API key from api.nal.usda.gov
- macOS 10.15+ (GUI installer) or Python 3.11+ (command line)
š¬ Example Usage
Once installed, ask Claude:
- "What's the nutrition info for salmon?"
- "Compare chicken breast vs tofu protein"
- "Find high-fiber breakfast foods"
- "Show vitamin content of spinach"
š ļø For Developers
Development Setup
# Clone the repository
git clone https://github.com/yourusername/usda-api-mcp.git
cd usda-api-mcp
# Install dependencies
uv sync
# Set up environment
echo "USDA_API_KEY=your_key_here" > .env
# Run the MCP server
uv run main.py
Project Structure
usda-api-mcp/
āāā main.py # MCP server implementation
āāā gui_installer.py # Web-based GUI installer
āāā install.sh # Command-line installer
āāā pyproject.toml # Dependencies
āāā create_app.sh # Build Mac app bundle
āāā dist/ # Distribution files
Available MCP Tools
search_foods(query, page_size, page_number)- Search food databaseget_food_details(fdc_id, nutrients)- Get detailed food informationget_multiple_foods(fdc_ids, nutrients)- Bulk food lookuplist_foods(data_type, page_size, page_number)- Browse foodsget_food_nutrients(fdc_ids, nutrients)- Get specific nutrients
Building Releases
# Build Mac app bundle
./create_app.sh
# Create distribution zip
cd dist && zip -r "USDA-Food-Tools-Installer.zip" "USDA Food Tools Installer.app"
# Test the app
open "USDA Food Tools Installer.app"
API Integration
The server integrates with USDA FoodData Central API v2:
- Base URL:
https://api.nal.usda.gov/fdc/v2/ - Authentication: API key in query parameters
- Rate Limits: 3600 requests/hour (free tier)
- Data Types: Foundation, SR Legacy, Survey, Branded foods
Contributing
- Fork the repository
- Create a feature branch:
git checkout -b feature-name - Make your changes and test thoroughly
- Submit a pull request
Testing
# Test MCP server directly
uv run main.py
# Test GUI installer
python3 gui_installer.py
# Test command-line installer
./install.sh
Deployment
The project uses a simple Mac app bundle approach:
- No complex packaging (py2app issues resolved)
- Shell script launcher with embedded Python
- Self-contained with port conflict resolution
- Automatic cleanup of existing processes
Server Terkait
MCP-NixOS
A server for searching NixOS, Home Manager, and nix-darwin resources.
Wikipedia
Retrieves information from Wikipedia to provide context to Large Language Models (LLMs).
Zenn Articles
A server for searching articles on the Zenn blogging platform.
GeoRanker
Access GeoRanker's SEO and keyword research tools for advanced search engine optimization analysis.
Bucketeer Docs Local MCP Server
A local server to query Bucketeer documentation, which automatically fetches and caches content from its GitHub repository.
eBird MCP Server
Query rich bird observation data from the eBird API using natural language.
MCP-MCP
A meta-server for discovering and provisioning other MCP servers from a large database.
Docs MCP
A server for efficiently searching and referencing user-configured local documents.
RateMySupervisor MCP
Query supervisor evaluation data with fuzzy matching for Chinese and Pinyin names.
Qdrant MCP Server
Semantic code search using the Qdrant vector database and OpenAI embeddings.