Which integrations are available for this server?

Allows searching and fetching real-time documentation for the LangChain library to provide up-to-date information for development and troubleshooting. Allows searching and fetching real-time documentation for OpenAI to provide up-to-date information for development and troubleshooting.

How do I use Library Docs MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Library Docs MCP Server find the latest documentation for Langchain tool calling" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Library Docs MCP Server

This is an MCP (Model Context Protocol) server that allows you to search and fetch documentation for popular libraries like Langchain, Llama-Index, MCP, and OpenAI using the Serper API.

Features

Search library documentation using a natural language query.
Supports Langchain, Llama-Index, MCP, and OpenAI (Update the code to add other libraries).
Uses the Serper API to perform site-specific searches.
Parses and returns the documentation using BeautifulSoup.
Provides updated documentation – useful for LLM models with knowledge cut-off dates.

Related MCP server: MCP Documentation Server

Why Use This Server with LLMs?

Many LLM models, including those used in Claude Desktop and similar platforms, have a knowledge cut-off date and may not have access to the latest library documentation. This MCP server solves that problem by:

Fetching real-time documentation from official sources.
Providing up-to-date information for development and troubleshooting.
Improving the accuracy and relevance of responses when working with new library updates.

Setting Up with Claude Desktop

To use this server with Claude Desktop, update the claude_desktop_config.json file with the following configuration:

{ "mcpServers": { "docs-mcp-server": { "command": "C:\\Users\\Vikram\\.local\\bin\\uv.exe", "args": [ "run", "--with", "mcp[cli]", "mcp", "run", "F:\\My Projects\\AI\\docs-mcp-server\\server.py" ] } } }