developer-tools

LLMS.txt Documentation

langchain-ai

by langchain-ai

LLMS.txt Documentation: Easily fetch and parse llms.txt files to provide instant AI-driven documentation lookup during c

Provides AI systems with access to documentation from llms.txt files by fetching and parsing content from specified URLs, enabling seamless documentation lookup during coding sessions.

github stars

942

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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Full control over documentation tool callsAuditable context retrieval process

best for

  • / Developers using AI coding assistants like Cursor or Claude
  • / Teams wanting auditable documentation retrieval
  • / Projects with llms.txt standardized documentation

capabilities

  • / List available llms.txt documentation sources
  • / Fetch documentation content from URLs or local files
  • / Parse llms.txt files into structured format
  • / Retrieve specific documentation sections on demand

what it does

Fetches and parses llms.txt documentation files from URLs, giving AI systems structured access to project documentation during coding sessions.

about

LLMS.txt Documentation is a community-built MCP server published by langchain-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. LLMS.txt Documentation: Easily fetch and parse llms.txt files to provide instant AI-driven documentation lookup during c It is categorized under developer tools. This server exposes 2 tools that AI clients can invoke during conversations and coding sessions.

how to install

You can install LLMS.txt Documentation in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

license

MIT

LLMS.txt Documentation is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

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 llms.txt to retrieve context for tasks. However, these apps use different built-in tools to read and process files like llms.txt. 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 llms.txt files and (2) a simple fetch_docs tool read URLs within any of the provided llms.txt files. This allows the user to audit each tool call as well as the context returned.

<img src="https://github.com/user-attachments/assets/736f8f55-833d-4200-b833-5fca01a09e1b" width="60%">

llms-txt

You can find llms.txt files for langgraph and langchain here:

Quickstart

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

Choose an llms.txt file to use.

  • For example, here's the LangGraph llms.txt file.

Note: Security and Domain Access Control

For security reasons, mcpdoc implements strict domain access controls:

  1. Remote llms.txt files: When you specify a remote llms.txt URL (e.g., https://langchain-ai.github.io/langgraph/llms.txt), mcpdoc automatically adds only that specific domain (langchain-ai.github.io) to the allowed domains list. This means the tool can only fetch documentation from URLs on that domain.

  2. Local llms.txt files: When using a local file, NO domains are automatically added to the allowed list. You MUST explicitly specify which domains to allow using the --allowed-domains parameter.

  3. Adding additional domains: To allow fetching from domains beyond those automatically included:

    • Use --allowed-domains domain1.com domain2.com to add specific domains
    • Use --allowed-domains '*' to allow all domains (use with caution)

This security measure prevents unauthorized access to domains not explicitly approved by the user, ensuring that documentation can only be retrieved from trusted sources.

(Optional) Test the MCP server locally with your llms.txt file(s) of choice:

uvx --from mcpdoc mcpdoc \
    --urls "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt" "LangChain:https://python.langchain.com/llms.txt" \
    --transport sse \
    --port 8082 \
    --host localhost

Screenshot 2025-03-18 at 3 29 30 PM

npx @modelcontextprotocol/inspector

Screenshot 2025-03-18 at 3 30 30 PM

  • Here, you can test the tool calls.

Connect to Cursor

  • Open Cursor Settings and MCP tab.
  • This will open the ~/.cursor/mcp.json file.

Screenshot 2025-03-19 at 11 01 31 AM

  • Paste the following into the file (we use the langgraph-docs-mcp name and link to the LangGraph llms.txt).
{
  "mcpServers": {
    "langgraph-docs-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "mcpdoc",
        "mcpdoc",
        "--urls",
        "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt LangChain:https://python.langchain.com/llms.txt",
        "--transport",
        "stdio"
      ]
    }
  }
}
  • Confirm that the server is running in your Cursor Settings/MCP tab.
  • Best practice is to then update Cursor Global (User) rules.
  • Open Cursor Settings/Rules and update User Rules with the following (or similar):
for ANY question about LangGraph, use the langgraph-docs-mcp server to help answer -- 
+ call list_doc_sources tool to get the available llms.txt file
+ call fetch_docs tool to read it
+ reflect on the urls in llms.txt 
+ reflect on the input question 
+ call fetch_docs on any urls relevant to the question
+ use this to answer the question
  • CMD+L (on Mac) to open chat.
  • Ensure agent is selected.

Screenshot 2025-03-18 at 1 56 54 PM

Then, try an example prompt, such as:

what are types of memory in LangGraph?

Screenshot 2025-03-18 at 1 58 38 PM

Connect to Windsurf

  • Open Cascade with CMD+L (on Mac).
  • Click Configure MCP to open the config file, ~/.codeium/windsurf/mcp_config.json.
  • Update with langgraph-docs-mcp as noted above.

Screenshot 2025-03-19 at 11 02 52 AM

  • Update Windsurf Rules/Global rules with the following (or similar):
for ANY question about LangGraph, use the langgraph-docs-mcp server to help answer -- 
+ call list_doc_sources tool to get the available llms.txt file
+ call fetch_docs tool to read it
+ reflect on the urls in llms.txt 
+ reflect on the input question 
+ call fetch_docs on any urls relevant to the question

Screenshot 2025-03-18 at 2 02 12 PM

Then, try the example prompt:

  • It will perform your tool calls.

Screenshot 2025-03-18 at 2 03 07 PM

Connect to Claude Desktop

  • Open Settings/Developer to update ~/Library/Application\ Support/Claude/claude_desktop_config.json.
  • Update with langgraph-docs-mcp as noted above.
  • Restart Claude Desktop app.

[!Note] If you run into issues with Python version incompatibility when trying to add MCPDoc tools to Claude Desktop, you can explicitly specify the filepath to python executable in the uvx command.

<details> <summary>Example configuration</summary>
{
  "mcpServers": {
    "langgraph-docs-mcp": {
      "command": "uvx",
      "args": [
        "--python",
        "/path/to/python",
        "--from",
        "mcpdoc",
        "mcpdoc",
        "--urls",
        "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt",
        "--transport",
        "stdio"
      ]
    }
  }
}
</details>

[!Note] Currently (3/21/25) it appears that Claude Desktop does not support rules for global rules, so appending the following to your prompt.

<rules>
for ANY question about LangGraph, use the langgraph-docs-mcp server to help answer -- 
+ call list_doc_sources tool to get the available llms.txt file
+ call fetch_docs tool to read it
+ reflect on the urls in llms.txt 
+ reflect on the input question 
+ call fetch_docs on any urls relevant to the question
</rules>

Screenshot 2025-03-18 at 2 05 54 PM

  • You will see your tools visible in the bottom right of your chat input.

Screenshot 2025-03-18 at 2 05 39 PM

Then, try the example prompt:

  • It will ask to approve tool calls as it processes your request.

Screenshot 2025-03-18 at 2 06 54 PM

Connect to Claude Code

  • In a terminal after installing Claude Code, run this command to add the MCP server to your project:
claude mcp add-json langgraph-docs '{"type":"stdio","command":"uvx" ,"args":["--from", "mcpdoc", "mcpdoc", "--urls", "langgraph:https://langchain-ai.github.io/langgraph/llms.txt", "LangChain:https://python.langchain.com/llms.txt"]}' -s local
  • You will see ~/.claude.json updated.
  • Test by launching Claude Code and running to view your tools:
$ Claude
$ /mcp 

Screenshot 2025-03-18 at 2 13 49 PM

[!Note] Currently (3/21/25) it appears that Claude Code does not support rules for global rules, so appending the following to your prompt.

<rules>
for ANY question about LangGraph, use the langgraph-docs-mcp server to help answer -- 
+ call list_doc_sources tool to get the available llms.txt file
+ call fetch_docs tool to read it
+ reflect on the urls in llms.txt 
+ reflect on the input question 
+ call fetch_docs on any

---

FAQ

What is the LLMS.txt Documentation MCP server?
LLMS.txt Documentation is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for LLMS.txt Documentation?
This profile displays 39 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.739 reviews
  • Kwame Chawla· Dec 28, 2024

    Useful MCP listing: LLMS.txt Documentation is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Alexander White· Dec 16, 2024

    According to our notes, LLMS.txt Documentation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Amelia Desai· Dec 16, 2024

    Strong directory entry: LLMS.txt Documentation surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Chaitanya Patil· Dec 4, 2024

    LLMS.txt Documentation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Piyush G· Nov 23, 2024

    According to our notes, LLMS.txt Documentation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Amelia Park· Nov 23, 2024

    LLMS.txt Documentation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Jin Yang· Nov 19, 2024

    LLMS.txt Documentation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • Amelia Dixit· Nov 7, 2024

    LLMS.txt Documentation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Tariq Taylor· Nov 7, 2024

    We wired LLMS.txt Documentation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Amelia Kapoor· Oct 26, 2024

    Strong directory entry: LLMS.txt Documentation surfaces stars and publisher context so we could sanity-check maintenance before adopting.

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