Server data from the Official MCP Registry
Checks food and cosmetic ingredients against each household member's allergies, incl. hidden names.
About
Checks food and cosmetic ingredients against each household member's allergies, incl. hidden names.
Remote endpoints: streamable-http: https://allergen-detector-mcp.onrender.com/mcp
Security Report
Valid MCP server (3 strong, 3 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry.
3 tools verified · Open access · No issues found
Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.
Permissions Found in Source Code
Found by scanning the linked source code. This listing connects to a hosted endpoint, so none of this runs on your machine: it describes what the server software does where it is hosted.
How to Connect
Remote Plugin
No local installation needed. Your AI client connects to the remote endpoint directly.
Add this to your MCP configuration to connect:
{
"mcpServers": {
"io-github-daniellemeng1022-allergen-detector": {
"url": "https://allergen-detector-mcp.onrender.com/mcp"
}
}
}Documentation
View on GitHubFrom the project's GitHub README.
Allergen Detector MCP
The Allergen Detector matching engine as an MCP server, so AI assistants and shopping agents (ChatGPT, Claude, and others) can check a product against a household's allergies before recommending or buying it.
It uses the same matcher.js and allergen map as the Chrome extension (51 allergens, 3,446 names in 24 languages). The map refreshes daily from GitHub, so map updates reach agents with no redeploy.
A short word that is an allergen in one language and an everyday word in another ("ou", "malta") only counts on text in its own language. The server infers the language from the "Ingredients" label word and, on text of 12 words or more, a detector; agents can also pass language. Short text with no label is not filtered. Words set aside are returned in other_language_words_not_counted, and languages_assumed shows the inference.
Tools
| Tool | What it does |
|---|---|
check_product_allergens | Checks an ingredient list against one or more people's allergens (with severity) and returns who is affected, by which ingredient, and why. "Free from" claims never hide a real ingredient. |
lookup_ingredient | Tells which allergen an ingredient name belongs to ("sodium caseinate" → milk). |
list_supported_allergens | Lists the allergens the service covers. |
The same checks are also a plain web API for agents that don't use MCP: POST /api/check, POST /api/lookup and GET /api/allergens, described at /openapi.json.
API keys are optional. Set API_KEYS="muse=<key>,acme=<key>" on the host to give each partner its own key, sent as X-API-Key or Authorization: Bearer. Requests without a key still work; a wrong key gets a 401. A valid key names the partner in /stats.
All tools are read-only and stateless. Nothing about the person (allergens, ingredients) is stored or logged. The only log is an anonymous count of tool calls per day, for measuring traction.
Run it
npm install
npm test # end-to-end test over MCP
npm start # remote server: http://localhost:3000/mcp
npm run stdio # local server for Claude Desktop and similar
npm run sync # copy the latest matcher.js and map from the extension
To use it locally in Claude Desktop, add this to the mcpServers section of its config:
"allergen-detector": { "command": "node", "args": ["/path/to/allergen-mcp/server.js", "--stdio"] }
Launch steps
- Host it at a public HTTPS URL. Render or Fly.io can run
npm startfrom a GitHub repo for $0–7/month. Later, pointmcp.safi.aiat it. - Privacy policy and terms page. Every directory requires one. Until safi.ai is live, host it on GitHub Pages. Its main point: no health data is stored.
- List it in the official MCP Registry (registry.modelcontextprotocol.io) with the
mcp-publisherCLI, underio.github.daniellemeng1022/allergen-detector. Many clients and directories pull from it. - Submit to the community directories: Smithery, Glama, PulseMCP and mcp.so.
- Claude: anyone can add it today as a custom connector by pasting the URL. To appear in Anthropic's connectors directory, submit it through their directory form.
- ChatGPT: test it in developer mode, then submit it as an app through OpenAI's Apps SDK submission process. This needs a verified developer account.
- Meta Muse (do this first): submit at muse.ai/platform. Choose "Existing MCP," enter the hosted URL, choose "API keys" and give Meta a key from
API_KEYS(e.g.muse=…) so its calls show up asmusein/statsand payments to "no," then describe the service. Meta tests the live endpoint, so step 1 has to be done first. Muse already shops at Sephora, Walmart, Best Buy and the Shopify catalog, which covers both of our categories (cosmetics and food). - Instinct and similar assistants: Instinct doesn't document custom MCP support. It shops through Shopify and Shop Pay. Reach out for a partnership instead.
- Shopping agents: contact teams building agentic-commerce products and offer the endpoint free during a pilot.
- Measure: set
STATS_KEYon the host, then openhttps://<your-url>/stats?key=<STATS_KEY>to see calls by day, agent and tool. Aconnectcount is an agent connecting; tool counts are real checks./statsresets when the server restarts, but every count is also a[usage]JSON line in the host's logs. Track weekly checks and the number of distinct agents.
Before scaling
- Add rate limiting and an API key for heavy business users. Keep a free tier.
- Keep the disclaimer in every response. Don't present results as medical advice.
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