FoodChecker

FoodChecker

An open-source food label checker for ChatGPT and Claude. Ask "is this healthy?" with a barcode, a product name or a photo of the label. You get a 0-100 score with the reasons, the way Yuka or Oasis would show it, but inside your chat and with a scoring method anyone can read.

Score	Rating
75-100	Excellent
50-74	Good
25-49	Poor
0-24	Bad

• Nutrition: based on Nutri-Score 2023.
• Additives: each one has a risk level and a cited reason.
• Processing: based on NOVA, with a note on why a food counts as ultra-processed.
• Alternatives: suggests healthier products in the same category.
• Data: over 3 million products from Open Food Facts. Labels you photograph are scored too, even products that aren't in the database.

How the score is built: docs/METHODOLOGY.md.

How it works

One MCP server serves both assistants. ChatGPT and Claude both implement the open MCP Apps standard, so the same score card renders inline in each of them:

ChatGPT / Claude  ──MCP──▶  FoodChecker server  ──▶  Open Food Facts API
       ▲                         │
       └──── score card (MCP Apps UI) + text summary

Tool	What it does
check_food	Look up a product by barcode or name, and score it
analyze_label	Score a product from its label (photo or pasted text), including US per-serving labels
find_healthier_alternatives	Find similar products that score clearly higher
explain_additive	Explain an additive by E-number or name ("E471", "Red 40") with its risk level and sources

All tools are read-only and need no login.

Use the hosted server

A public instance runs at https://foodchecker-ebon.vercel.app/mcp. Paste that URL into ChatGPT or Claude as shown below. You can try the score card in a browser at https://foodchecker-ebon.vercel.app.

Run it locally

npm install
npm run dev

• Open http://localhost:3000/preview?q=coca-cola to see the score card in a browser.
• npm test runs the scoring tests.
• npm run smoke calls every tool over MCP against live data while the server is running.

Add it to ChatGPT

Use the hosted URL or your own deployment. ChatGPT can't reach localhost.

1. Go to chatgpt.com/plugins, click +, then Add custom MCP server.
2. Server URL: https://foodchecker-ebon.vercel.app/mcp. Authentication: No authentication.
3. Create it, then ask: "Is Nutella healthy?"

To list it in the ChatGPT app directory, follow OpenAI's submission flow at platform.openai.com/plugins. FoodChecker already supports two of its requirements through environment variables:

• OPENAI_APPS_CHALLENGE: the domain-verification token, served at /.well-known/openai-apps-challenge.
• CHATGPT_WIDGET_DOMAIN: your widget origin.

You'll also need a privacy policy URL, a terms URL, test prompts and a demo video.

Add it to Claude

• claude.ai or Claude Desktop: go to Customize → Connectors → Add custom connector, paste https://foodchecker-ebon.vercel.app/mcp, and choose No sign-in. The score card renders on web, desktop and mobile.

• Claude Code, remote server: run claude mcp add --transport http foodchecker https://foodchecker-ebon.vercel.app/mcp.

• Claude Code, local server: run claude mcp add foodchecker -- npx tsx /path/to/repo/src/stdio.ts.

• Claude Code plugin with a usage skill: this repo is a plugin marketplace. It points at the hosted server. If you self-host, change the URL in plugins/foodchecker/.mcp.json. Then run:

  /plugin marketplace add kjagsadvisors/foodchecker
  /plugin install foodchecker@foodchecker
  

Claude Code shows text, not the score card. The text summary carries the same information.

Deploy

The server is a standard web Request → Response handler (src/app.ts), so it runs almost anywhere.

• Docker (Render, Fly.io, Railway, any VPS): docker build -t foodchecker . && docker run -p 3000:3000 foodchecker
• Vercel: vercel deploy. vercel.json builds the project and routes everything to api/index.js.
• Plain Node: npm run build && npm start

Set OFF_USER_AGENT to FoodChecker/0.1.0 (your contact email). Open Food Facts asks every app to identify itself. See .env.example for the other settings.

Rate limits: Open Food Facts allows roughly 15 product lookups and 10 searches per minute per IP. FoodChecker caches results in memory (products for 12 hours, searches for 1 hour). For heavy traffic, load the Open Food Facts data dump into your own database.

Project layout

src/core/        scoring engine: Nutri-Score 2023, additive table, ingredient parser, 0-100 score
src/off/         Open Food Facts client and data normalisation
src/mcp/         MCP server, tool definitions, score-card widget, text summaries
src/app.ts       web handler: /mcp, /preview, landing page
src/http.ts      Node server       src/stdio.ts   local stdio server
plugins/         Claude Code plugin (MCP config + food-check skill)
test/            scoring tests     scripts/       smoke test, dev MCP Apps host

Contributing

The most useful contributions are:

• Additive table (src/core/additives.ts): challenge a risk level with a source, or add a missing additive and its label names in your language.
• Ingredient parsing (src/core/ingredients.ts): label wording that gets missed. Add a test case in test/scoring.test.ts.
• Product data: fix the product on Open Food Facts and everyone benefits.

npm run dev:host builds a small MCP Apps host on the official AppBridge. Then open /dev/host?barcode=… to check the widget the way ChatGPT and Claude embed it.

License and data

• Code: MIT.
• Product data: © Open Food Facts contributors, under the Open Database License. Product images are under CC BY-SA. Keep the attribution that the score card and text summaries show.

FoodChecker gives general information about packaged food. It is not medical advice.