This is an implementation of the Model Context Protocol (MCP) for the Michelin dataset. It can be integrated with an LLM using the MCP protocol via
This is an implementation of the Model Context Protocol (MCP) for the Michelin dataset. It can be integrated with an LLM using the MCP protocol via the SSE deployment at the endpoint https://mcp.michelin.com. The michelin mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it through 6 defined tools rather than through you.
Everything the assistant can do here goes through one of these:
getAllCity — Get a list of all cities ordered by the number of restaurants in descending order. Call again with the next offset to get more citiesgetAllCountry — Get a list of all countries ordered by the number of restaurants in descending order. Call again with the next offset to get more countriesgetAllCuisine — Get a list of all cuisines ordered by the number of restaurants in descending order. Call again with the next offset to get more cuisinesgetAllAward — Get a list of all awards ordered by ranking ascendinggetAllFacilitiesAndServices — Get a list of all facilities and services ordered by the number of restaurants in descending order. Call again with the next offset to get moregetAllRestaurant — Get a list of all restaurants with given filters. Do not provide empty strings or null values as filters. Omit the filter to get all restaurantsBeing a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Michelin MCP's toolset — getAllCity, getAllCountry, getAllCuisine and 3 more — is a fair guide to whether it matches your workflow. It is maintained by hrishabhn; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Michelin MCP's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| getAllCity | Get a list of all cities ordered by the number of restaurants in descending order. Call again with the next offset to get more cities. |
| getAllCountry | Get a list of all countries ordered by the number of restaurants in descending order. Call again with the next offset to get more countries. |
| getAllCuisine | Get a list of all cuisines ordered by the number of restaurants in descending order. Call again with the next offset to get more cuisines. |
| getAllAward | Get a list of all awards ordered by ranking ascending. |
| getAllFacilitiesAndServices | Get a list of all facilities and services ordered by the number of restaurants in descending order. Call again with the next offset to get more facilities and services. |
| getAllRestaurant | Get a list of all restaurants with given filters. Do not provide empty strings or null values as filters. Omit the filter to get all restaurants. |
{
"mcpServers": {
"Michelin": {
"command": "npx",
"args": ["supergateway", "--sse", "https://michelin-mcp.deno.dev"]
}
}
}Configuration as documented by the project. Restart the client after saving.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.