About MCP Documentation Server
An MCP documentation server for hosts such as Cursor, Claude Desktop, and Windsurf. It provides access to LangGraph and MCP documentation files and can fetch content from URLs referenced in them.
Key features
- 01List available documentation files
- 02Read LangGraph and MCP documents
- 03Fetch documentation by URL
- 04Restrict accessible domains
Use cases
- 01Look up LangGraph documentation
- 02Find information about MCP
- 03Fetch details from documentation links
MCP support
As an MCP server, it provides host applications with tools to list documentation sources and retrieve documents. It can also fetch content from URLs referenced in the configured files.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEMCP Documentation Server
A customized version of the MCP documentation server that enables integration between LLM applications (like Cursor, Claude Desktop, Windsurf) and documentation sources via the Model Context Protocol.
Overview
This server provides MCP host applications with:
- Access to specific documentation files (langgraph.txt and mcp.txt)
- Tools to fetch documentation from URLs within those files
Supported Documentation
Currently set up for:
- LangGraph Documentation (from https://raw.githubusercontent.com/esakrissa/mcp-doc/main/docs/langgraph.txt)
- MCP Documentation (from https://raw.githubusercontent.com/esakrissa/mcp-doc/main/docs/mcp.txt)
Quick Start
Setup and Run
# Clone the repository
git clone https://github.com/esakrissa/mcp-doc.git
cd mcp-doc
# Create and activate a virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install the package in development mode
pip install -e .
Running the Server
You can run the server using the installed command:
# Run the server with the config file
mcpdoc \
--json config.json \
--transport sse \
--port 8082 \
--host localhost
Or if you prefer using UV:
# Install uv (if not already installed)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Run the server with UV
uvx --from mcpdoc mcpdoc \
--json config.json \
--transport sse \
--port 8082 \
--host localhost
IDE Integration
Cursor
Add to ~/.cursor/mcp.json
{
"mcpServers": {
"mcp-doc": {
"command": "uvx",
"args": [
"--from",
"mcpdoc",
"mcpdoc",
"--urls",
"LangGraph:https://raw.githubusercontent.com/esakrissa/mcp-doc/main/docs/langgraph.txt",
"ModelContextProtocol:https://raw.githubusercontent.com/esakrissa/mcp-doc/main/docs/mcp.txt",
"--allowed-domains",
"*",
"--transport",
"stdio"
]
}
}
}
Then add these instructions to Cursor's Custom Instructions:
for ANY question about LangGraph and Model Context Protocol (MCP), use the mcp-doc server to help answer --
+ call list_doc_sources tool to get the available documentation files
+ call fetch_docs tool to read the langgraph.txt or mcp.txt file
+ reflect on the urls in langgraph.txt or mcp.txt
+ reflect on the input question
+ call fetch_docs on any urls relevant to the question
+ use this to answer the question
To test if the integration is working, ask Cursor a question about LangGraph or MCP, and check if it uses the documentation server tools to fetch information.
Security Note
For security reasons, strict domain access controls are implemented:
- Remote documentation files: Only the specific domain is automatically allowed
- Local documentation files: No domains are automatically allowed
- Use
--allowed-domainsto explicitly add domains or--allowed-domains '*'to allow all (use with caution)
References
This project is based on the original mcpdoc by LangChain AI, modified to provide focused documentation access for LangGraph and MCP.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.