NutriRef
USDA nutrition API optimized for AI agents. x402 + USDC on Base
README
NutriRef
Pay-per-call USDA nutrition data for AI agents. Structured FoodData Central via the x402 micropayment protocol — agents pay $0.001–$0.005 in USDC per request, no signup, no API keys, no human auth flows.
Live at https://nutriref.xyz. Spec at /openapi.json · Swagger at /docs · Bazaar discovery at /.well-known/x402.
Endpoints
| Method | Path | Price | Cache |
|---|---|---|---|
GET |
/v1/nutrition/search?q=&limit= |
$0.001 | 24h |
GET |
/v1/nutrition/detail/{fdc_id} |
$0.002 | 7d |
POST |
/v1/nutrition/compare |
$0.003 | derived |
POST |
/v1/nutrition/recipe |
$0.005 | derived |
All values per 100g. Missing nutrients are null, not 0. compare returns per-nutrient winners (highest protein, lowest sodium, etc.). recipe scales by grams and sums.
Use it from Claude (or any MCP agent)
NutriRef ships an MCP server that exposes the four endpoints as native tools. Install once:
git clone https://github.com/Younghef/nutriref-api.git
cd nutriref-api
pip install -e ".[mcp]"
A PyPI release is planned. Until then the source install above is the supported path.
Then add this to your MCP client config (Claude Desktop's claude_desktop_config.json, Claude Code's MCP settings, etc.):
{
"mcpServers": {
"nutriref": {
"command": "python",
"args": ["-m", "mcp_server"],
"env": {
"PAYER_PRIVATE_KEY": "0x...your-funded-wallet-key...",
"NUTRIREF_BASE_URL": "https://nutriref.xyz"
}
}
}
}
The wallet needs USDC on Base mainnet — gas is sponsored by the facilitator, so you only need stablecoin balance. The agent now has nutrition_search, nutrition_detail, nutrition_compare, nutrition_recipe and auto-pays per call.
Use it from any HTTP client
Unpaid requests get 402 Payment Required with x402 payment instructions. Any x402-aware client signs a gasless USDC authorization (EIP-3009) and retries automatically:
import asyncio
from eth_account import Account
from x402.client import x402Client
from x402.http.clients.httpx import wrapHttpxWithPayment
from x402.mechanisms.evm.exact import register_exact_evm_client
account = Account.from_key("0x...funded-wallet-key...")
client = x402Client(); register_exact_evm_client(client, account)
async def main():
async with wrapHttpxWithPayment(client, base_url="https://nutriref.xyz") as http:
r = await http.get("/v1/nutrition/detail/2012128")
print(r.json())
asyncio.run(main())
Response example
GET /v1/nutrition/detail/173944:
{
"fdc_id": 173944,
"description": "Banana, raw",
"data_type": "Foundation",
"serving_size": 100, "serving_size_unit": "g",
"calories": 89.0, "protein": 1.1, "fat": 0.3,
"carbs": 22.8, "fiber": 2.6, "sugar": 12.2,
"sodium": 1.0, "cholesterol": null, "saturated_fat": 0.1,
"vitamin_c": 8.7, "calcium": 5.0, "iron": 0.3, "potassium": 358.0
}
Self-hosting
NutriRef is open source; the live instance at nutriref.xyz is one deployment among many possible. To run your own:
cp .env.example .env
# fill in USDA_API_KEY (free at https://fdc.nal.usda.gov/api-key-signup.html)
# and X402_RECEIVER_ADDRESS (an EVM address that should receive payments)
docker compose up --build
curl http://localhost:8000/health
Configuration
| Var | Required | Default | Purpose |
|---|---|---|---|
USDA_API_KEY |
yes | — | Free key from fdc.nal.usda.gov |
USDA_BASE_URL |
no | https://api.nal.usda.gov/fdc/v1 |
|
REDIS_URL |
no | redis://redis:6379/0 |
Response cache |
X402_NETWORK |
no | base-sepolia |
base for mainnet |
X402_RECEIVER_ADDRESS |
yes | — | EVM address that receives USDC |
X402_FACILITATOR_URL |
no | https://x402.org/facilitator |
https://api.cdp.coinbase.com for mainnet |
CDP_API_KEY_ID |
mainnet only | — | Coinbase Developer Platform key ID |
CDP_API_KEY_SECRET |
mainnet only | — | Coinbase Developer Platform key secret |
LOG_LEVEL |
no | INFO |
For mainnet you need a Coinbase CDP account and the public x402 facilitator at https://api.cdp.coinbase.com. Testnet works for free with the community facilitator at https://x402.org/facilitator.
Architecture
agent → x402 middleware → route handler → cache (Redis) → USDA FDC API
search and detail cache USDA responses directly. compare and recipe compose from the cached detail data — no extra USDA calls when warm. The cache is a meaningful cost lever: warm requests return in <50ms and never hit USDA.
Tests
pip install -e ".[dev]"
pytest
Example: Claude agent that uses NutriRef
examples/meal-planner/ is a complete, ~150-line agent that gives Claude
the four NutriRef endpoints as tools and asks it to plan a day of meals
hitting a calorie/protein goal. Worth reading if you're wiring NutriRef
into your own agent — the tool schemas and the payment loop are all
there. See examples/meal-planner/README.md.
Repo layout
app/ # FastAPI service
main.py # app factory + x402 init
routes/ # search, detail, compare, recipe
landing.py # / (public landing page)
discovery.py # /.well-known/x402, /llms.txt, /.well-known/ai-plugin.json, /logo.svg
usda.py # async USDA client
cache.py # Redis wrapper
normalize.py # USDA → flat 13-nutrient schema
mcp_server/ # MCP server wrapper for agent use
examples/ # worked agent examples (meal planner)
scripts/ # CDP wallet bootstrap + payer-side test
tests/ # pytest + respx + fakeredis
Acknowledgments
- USDA FoodData Central for the data.
- x402 for the payment protocol.
fastapi-x402for the server middleware (with a small EIP-712 patch we apply at startup for Base mainnet USDC).
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.