mcpdoc — MCP Server by langchain-ai

by langchain-ai · MCP Server · ★ 982

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About mcpdoc

MCP LLMS-TXT Documentation Server Overview llms.txt is a website index for LLMs, providing background information, guidance, and links to detailed markdown files. IDEs like Cursor and Windsurf or apps like Claude Code/Desktop can use to retrieve context for tasks. However, these apps use different built-in tools to read and process files like . The retrieval process can be opaque, and there is not always a way to audit the tool calls or the context returned. MCP offers a way for developers to have full control over tools used by these applications. Here, we create an open source MCP server to provide MCP host applications (e.g., Cursor, Windsurf, Claude Code/Desktop) with (1) a user-defined list of files and (2) a simple tool read URLs within any of the provided files. This allows the user to audit each tool call as well as the context returned. llms-txt You can find llms.txt files for langgraph and langchain here: [

agentsclaude-codecursoridellmsllms-txt

Quick Facts

Stars982
Forks121
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score51.656/100
Open Issues14
Last Updated2026-05-02
Created2025-03-18
Platformsclaude-code, mcp, python
Est. Tokens~24k

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Frequently Asked Questions

What is mcpdoc?

mcpdoc is Expose llms-txt to IDEs for development. It is categorized as a MCP Server with 982 GitHub stars.

What programming language is mcpdoc written in?

mcpdoc is primarily written in Python. It covers topics such as agents, claude-code, cursor.

How do I install or use mcpdoc?

You can find installation instructions and usage details in the mcpdoc GitHub repository at github.com/langchain-ai/mcpdoc. The project has 982 stars and 121 forks, indicating an active community.

What license does mcpdoc use?

mcpdoc is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to mcpdoc?

The top alternatives to mcpdoc on Agent Skills Hub include crystal, ruler, collab-public. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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