mcpField
L
llmmcp

llmmcp

Local
github.com/zero-abdDeveloper Tools6Updated 36w ago
Repository

About llmmcp

llmmcp is an MCP server that gives coding agents searchable documentation for OpenAI, Anthropic, and Google Gemini. It indexes official materials and dynamically lists providers and promoted models, helping verify API details and implementation patterns.

Key features

  1. 01Search official provider documentation
  2. 02List providers and models dynamically
  3. 03Look up API parameters and limits
  4. 04Retrieve current implementation patterns

Use cases

  1. 01Check whether a model supports a feature
  2. 02Verify tool-use parameters
  3. 03Confirm context windows and rate limits
  4. 04Avoid deprecated library patterns

MCP support

The MCP server provides `search_docs` for documentation queries and `list_providers` for provider listings. Its documentation index is refreshed weekly from official materials.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

llmmcp

MCP Registry

🌐 Website: https://llmmcp.vercel.app
🎥 Demo:

https://github.com/user-attachments/assets/eaad8d05-b7a8-4bf0-86c6-4fe2726da628

Stop LLM hallucinations and outdated code patterns.

llmmcp is a Model Context Protocol (MCP) server that provides real-time, up-to-date documentation for major LLM providers (OpenAI, Anthropic, and Google Gemini). It ensures your AI agents—like Cursor, Claude Desktop, or Windsurf—base their work on current official documentation instead of stale training data or deprecated library patterns.

Why use llmmcp?

LLMs frequently hallucinate about their own latest versions, feature availability (e.g., tool use in certain models), and pricing. llmmcp fixes this by providing:

  • ✅ Up-to-Date Model Info: Always know the latest available models (e.g., Gemini 2.0 Flash, Claude 3.5 Sonnet).
  • ✅ Detailed API Params: Verified tool use syntax, context window sizes, and rate limits.
  • ✅ Latest Implementation Patterns: Force your AI agent to follow current best practices instead of using legacy or deprecated library versions.
  • ✅ Real-Time Search: Queries an indexed vector database of official provider documentation.
  • ✅ Dynamic Listings: Get the current state of providers without hardcoded lists.

🚀 Quick Start

You can use llmmcp immediately in your favorite AI tools without local installation.

Cursor

Add a new MCP server in Settings > Models > MCP Servers:

  • Name: llmmcp
  • Type: command
  • Command: npx -y llmmcp@latest
Claude Desktop

Add the following to your claude_desktop_config.json:

{
  "mcpServers": {
    "llmmcp": {
      "command": "npx",
      "args": ["-y", "llmmcp@latest"]
    }
  }
}

🛠 Features

search_docs

Search the latest official documentation for specific technical details. Example: "What are the tool use parameters for Gemini 1.5 Pro?"

list_providers

Get a dynamically updated list of available providers (OpenAI, Anthropic, Google) and their currently promoted models.

🏗 How it Works

llmmcp is designed for speed and reliability:

  1. Indexer: A weekly scraper fetches raw markdown/text from official documentation.
  2. Vector DB: Chunks are embedded and stored in Pinecone with integrated embedding support.
  3. Backend: A Cloudflare Worker handles query embedding and retrieval, caching frequent results in Workers KV.
  4. MCP Client: A thin CLI translates MCP requests into API calls for the Worker.

🤝 Contributing & Self-Hosting

This project is open-source. If you'd like to run your own instance of the backend:

  1. See Architecture & Deployment (coming soon, see current setup in logs).
  2. Fork the repo and submit a PR for new documentation sources.

Developed by Abdullah Al Mahmud

License

MIT

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.