MCP server
by langchain-ai
LLMS.txt Documentation: Easily fetch and parse llms.txt files to provide instant AI-driven documentation lookup during c
Fetches and parses llms.txt documentation files from URLs, giving AI systems structured access to project documentation during coding sessions.
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.
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.
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.
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
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
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
Share your MCP server with the developer community
Useful MCP listing: LLMS.txt Documentation is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, LLMS.txt Documentation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Strong directory entry: LLMS.txt Documentation surfaces stars and publisher context so we could sanity-check maintenance before adopting.
LLMS.txt Documentation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
According to our notes, LLMS.txt Documentation benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
LLMS.txt Documentation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
LLMS.txt Documentation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
LLMS.txt Documentation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We wired LLMS.txt Documentation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: LLMS.txt Documentation surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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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.
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 |
uv.curl -LsSf https://astral.sh/uv/install.sh | sh
llms.txt file to use.llms.txt file.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.
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
npx @modelcontextprotocol/inspector
tool calls.Cursor Settings and MCP tab.~/.cursor/mcp.json file.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"
]
}
}
}
Cursor Settings/MCP tab.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.agent is selected.Then, try an example prompt, such as:
what are types of memory in LangGraph?
CMD+L (on Mac).Configure MCP to open the config file, ~/.codeium/windsurf/mcp_config.json.langgraph-docs-mcp as noted above.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
Then, try the example prompt:
Settings/Developer to update ~/Library/Application\ Support/Claude/claude_desktop_config.json.langgraph-docs-mcp as noted above.[!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
<details> <summary>Example configuration</summary>pythonexecutable in theuvxcommand.</details>{ "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>
Then, try the example prompt:
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
~/.claude.json updated.$ 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
---
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ 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.