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Llmmcp

Searches current OpenAI, Anthropic, and Google Gemini documentation through MCP tools.

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Install Llmmcp

Pick your client, then add it in one click or paste the config.

STDIONode.js
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "llmmcp": {
      "command": "npx",
      "args": [
        "-y",
        "llmmcp@latest"
      ]
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Automated check passed— started and listed 2 tools correctly (2d ago).

Overview

The llmmcp MCP server gives AI clients searchable, current documentation for OpenAI, Anthropic, and Google Gemini APIs. It exposes tools for searching indexed provider documentation and listing providers with their promoted models. A weekly indexing process collects official documentation, stores embeddings in Pinecone, and serves retrieval through a Cloudflare Worker with Workers KV caching. Reach for it when an agent needs current model details, API parameters, or implementation guidance instead of relying on stale training data.

Use cases

  • Search current tool-use parameters for an LLM provider
  • Check promoted models before generating integration code
  • Verify context limits and rate-limit details
  • Replace outdated provider API patterns in an agent workflow

Key features

  • Searches indexed official provider documentation
  • Lists providers and promoted models dynamically
  • Covers OpenAI, Anthropic, and Google Gemini
  • Refreshes documentation sources weekly
  • Uses Pinecone vector retrieval with Workers KV caching

Tools2

What Llmmcp lets your agent do. Expand a tool for its parameters.

Verified live~211 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 2d ago

Recent health check succeeded.

6/6 checks healthy over the last 50d

What it can do

From the 2 tools this server returned when we ran it.

Read-only tools · 2
Network at startup3 connections
Files written at startupNone
Install scriptsNone
npm provenanceNot published
Package age251 days
Maintainers1

Observed 2026-10-26 in an isolated sandbox. A description of reach, not a security audit.

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 12d ago via OSV.dev · llmmcp@latest (npm)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars6Stargazers on the source repository.
npm downloads39Package downloads in the last 30 days.
Last commit8mo agoMost recent push to the default branch.
Availability100%Our rolling endpoint + install checks that succeeded.
Install checkPassedOur sandbox started it and listed its tools.
Tools exposed2Callable tools this server registers over MCP.

How Llmmcp works

What llmmcp MCP server does

The llmmcp MCP server exposes current documentation for three major LLM providers: OpenAI, Anthropic, and Google Gemini. It is intended for AI coding clients that need dependable answers about model availability, API behavior, tool-use syntax, context limits, rate limits, and current implementation patterns.

The service addresses a common documentation problem: an agent may produce code based on deprecated libraries, unavailable models, or older API conventions. Rather than embedding a fixed provider list in the client, llmmcp retrieves provider and model information dynamically.

How it works

An indexing process runs weekly and collects Markdown or text from official provider documentation. The collected material is divided into chunks, embedded, and stored in Pinecone. Pinecone's integrated embedding support is used for the vector database layer.

When an MCP client sends a search request, a Cloudflare Worker handles query embedding and document retrieval. Frequently requested results can be cached in Cloudflare Workers KV. The command-line client acts as a thin MCP layer that forwards requests to the worker's API.

This architecture means search results come from an indexed documentation corpus rather than from the language model's built-in knowledge alone. The index is refreshed weekly, so the service is designed for current reference information but is not described as an instant mirror of provider documentation.

Setup and configuration

The llmmcp MCP server can be launched through npx, without a separate local installation. For Cursor, add an MCP server named llmmcp in the MCP server settings and use npx -y llmmcp@latest as the command.

Claude Desktop can use the same package through an MCP configuration entry with npx as the command and -y, llmmcp@latest as its arguments. The README also identifies Windsurf as a supported agent client, although it does not provide a separate Windsurf configuration example.

The provided setup instructions do not require environment variables or API keys for client use. Self-hosting the backend is mentioned as a contribution option, with deployment details referred to in the repository documentation.

Tools and capabilities

  • search_docs searches the latest indexed official documentation for specific technical questions, such as tool-use parameters for a particular Gemini model.
  • list_providers returns a dynamically updated provider list and the models currently promoted by those providers.

These tools make the llmmcp MCP server suitable for agents that generate or review integrations with OpenAI, Anthropic, or Google Gemini. The available sources are limited to the providers and documentation indexed by the project.

Limitations and notes

It does not document arbitrary web search, access to private provider documentation, or direct execution of provider API calls. The documentation corpus is refreshed weekly, so changes made after the latest indexing run may not be present immediately.

The project is open source and licensed under MIT.

Read the full README View source on GitHub

Reviews

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Frequently Asked Questions about Llmmcp

llmmcp is an MCP server that connects AI clients to current official documentation from OpenAI, Anthropic, and Google Gemini. Its main tools are search_docs, which retrieves technical details from indexed provider documentation, and list_providers, which reports available providers and promoted models. It helps agents use current API information.

Run it through npx with `npx -y llmmcp@latest` rather than installing it separately. In Cursor, add that command as an MCP server; in Claude Desktop, configure `npx` with `-y` and `llmmcp@latest` as the arguments.

Yes, llmmcp works with Cursor through its MCP server settings. Add a server named `llmmcp`, select the command-based configuration, and use `npx -y llmmcp@latest` as the command.

llmmcp provides documentation for OpenAI, Anthropic, and Google Gemini. Its `list_providers` tool reports the currently available providers and their promoted models.

The documentation index is refreshed weekly. A Cloudflare Worker retrieves embedded content from the Pinecone vector database, while frequently used results may be cached in Workers KV.

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