
LLMS.txt Documentation
Fetches and parses llms.txt documentation files from URLs, giving AI systems structured access to project documentation during coding sessions.
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.
What it does
- 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
Best for
About LLMS.txt Documentation
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
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.
Tools (2)
List all available documentation sources. This is the first tool you should call in the documentation workflow. It provides URLs to llms.txt files or local file paths that the user has made available. Returns: A string containing a formatted list of documentation sources with their URLs or file paths
Fetch and parse documentation from a given URL or local file. Use this tool after list_doc_sources to: 1. First fetch the llms.txt file from a documentation source 2. Analyze the URLs listed in the llms.txt file 3. Then fetch specific documentation pages relevant to the user's question Args: url: The URL to fetch documentation from. Returns: The fetched documentation content converted to markdown, or an error message if the request fails or the URL is not from an allowed domain.
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.
llms-txt
You can find llms.txt files for langgraph and langchain here:
| Library | llms.txt |
|---|---|
| LangGraph Python | https://langchain-ai.github.io/langgraph/llms.txt |
| LangGraph JS | https://langchain-ai.github.io/langgraphjs/llms.txt |
| LangChain Python | https://python.langchain.com/llms.txt |
| LangChain JS | https://js.langchain.com/llms.txt |
Quickstart
Install uv
- Please see official uv docs for other ways to 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.txtfile.
Note: Security and Domain Access Control
For security reasons, mcpdoc implements strict domain access controls:
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.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-domainsparameter.Adding additional domains: To allow fetching from domains beyond those automatically included:
- Use
--allowed-domains domain1.com domain2.comto 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
- This should run at: http://localhost:8082
- Run MCP inspector and connect to the running server:
npx @modelcontextprotocol/inspector
- Here, you can test the
toolcalls.
Connect to Cursor
- Open
Cursor SettingsandMCPtab. - This will open the
~/.cursor/mcp.jsonfile.
- Paste the following into the file (we use the
langgraph-docs-mcpname and link to the LangGraphllms.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/MCPtab. - Best practice is to then update Cursor Global (User) rules.
- Open Cursor
Settings/Rulesand updateUser Ruleswith 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
agentis selected.
Then, try an example prompt, such as:
what are types of memory in LangGraph?
Connect to Windsurf
- Open Cascade with
CMD+L(on Mac). - Click
Configure MCPto open the config file,~/.codeium/windsurf/mcp_config.json. - Update with
langgraph-docs-mcpas noted above.
- Update
Windsurf Rules/Global ruleswith 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
Then, try the example prompt:
- It will perform your tool calls.
Connect to Claude Desktop
- Open
Settings/Developerto update~/Library/Application\ Support/Claude/claude_desktop_config.json. - Update with
langgraph-docs-mcpas 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
pythonexecutable in theuvxcommand.Example configuration
{ "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" ] } } }
[!Note] Currently (3/21/25) it appears that Claude Desktop does not support
rulesfor 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>
- You will see your tools visible in the bottom right of your chat input.
Then, try the example prompt:
- It will ask to approve tool calls as it processes your request.
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.jsonupdated. - Test by launching Claude Code and running to view your tools:
$ Claude
$ /mcp
[!Note] Currently (3/21/25) it appears that Claude Code does not support
rulesfor 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
---
*README truncated. [View full README on GitHub](https://github.com/langchain-ai/mcpdoc).*
Alternatives
Related Skills
Browse all skillsUI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.
Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
Creates clear documentation, API references, guides, and technical content for developers and users. Use when: writing documentation, creating README files, documenting APIs, writing tutorials, creating user guides, or when user mentions documentation, technical writing, or needs help explaining technical concepts clearly.
Best practices for creating, optimizing, and deploying MCP servers to Smithery. Use this skill when:(1) Creating new MCP servers for Smithery deployment(2) Optimizing quality scores (achieving 90/100)(3) Troubleshooting deployment issues (0/0 tools, missing annotations, low scores)(4) Migrating existing MCP servers to Smithery format(5) Understanding Smithery's schema format requirements(6) Adding workflow prompts, tool annotations, or documentation resources(7) Configuring smithery.yaml and package.json for deployment
Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".