Documentation Markdown Converter MCP server

The MCP LLMS-TXT Documentation Server provides a way to connect AI coding assistants like Cursor, Windsurf, and Claude to your chosen documentation sources via the Model Context Protocol (MCP). This server allows you to specify llms.txt files and provides tools for applications to fetch and use that documentation.

Installation

Install UV Package Manager

First, install the UV package manager:

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

For alternative installation methods, refer to the official UV documentation.

Select an llms.txt File

Choose an llms.txt file to use with your MCP server. For example, you might use the LangGraph documentation:

• LangGraph: https://langchain-ai.github.io/langgraph/llms.txt

Testing the Server

Launch the Server Locally

Test your MCP server with your chosen llms.txt file:

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

The server will be available at http://localhost:8082

Test with MCP Inspector

You can use the MCP Inspector to test tool calls:

npx @modelcontextprotocol/inspector

Connecting to AI Applications

Cursor Setup

1. Open Cursor Settings and navigate to the MCP tab
2. This opens the ~/.cursor/mcp.json file
3. Add the following configuration:

{
  "mcpServers": {
    "langgraph-docs-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "mcpdoc",
        "mcpdoc",
        "--urls",
        "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt",
        "--transport",
        "stdio",
        "--port",
        "8081",
        "--host",
        "localhost"
      ]
    }
  }
}

4. Verify the server is running in the MCP tab
5. Open chat with CMD+L (on Mac)
6. Select "agent" mode
7. Try a sample prompt:

use the langgraph-docs-mcp server to answer any LangGraph questions -- 
+ 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

what are types of memory in LangGraph?

Windsurf Setup

1. Open Cascade with CMD+L (on Mac)
2. Click "Configure MCP" to open ~/.codeium/windsurf/mcp_config.json
3. Update with the same configuration as shown for Cursor
4. Open Cascade and refresh MCP servers
5. Try the example prompt

Claude Desktop Setup

1. Open Settings/Developer to update ~/Library/Application\ Support/Claude/claude_desktop_config.json
2. Add the same server configuration as shown for Cursor
3. Restart Claude Desktop
4. You'll see your tools in the bottom right of the chat input
5. Try the example prompt

Claude Code Setup

1. After installing Claude Code, run:

claude mcp add-json langgraph-docs '{"type":"stdio","command":"uvx" ,"args":["--from", "mcpdoc", "mcpdoc", "--urls", "langgraph:https://langchain-ai.github.io/langgraph/llms.txt"]}' -s local

2. This updates ~/.claude.json
3. Launch Claude Code and run:

$ Claude
$ /mcp 

4. Try the example prompt

Command-Line Interface

The mcpdoc command provides several ways to specify documentation sources:

Using a YAML Config File

mcpdoc --yaml sample_config.yaml

Using a JSON Config File

mcpdoc --json sample_config.json

Directly Specifying URLs

mcpdoc --urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt

Combining Methods

mcpdoc --yaml sample_config.yaml --json sample_config.json --urls https://langchain-ai.github.io/langgraph/llms.txt

Additional Options

• --follow-redirects: Follow HTTP redirects (defaults to False)
• --timeout SECONDS: HTTP request timeout in seconds (defaults to 10.0)

Example with options:

mcpdoc --yaml sample_config.yaml --follow-redirects --timeout 15

Configuration Format

YAML Configuration Example

# Sample configuration for mcp-mcpdoc server
# Each entry must have a llms_txt URL and optionally a name
- name: LangGraph Python
  llms_txt: https://langchain-ai.github.io/langgraph/llms.txt

JSON Configuration Example

[
  {
    "name": "LangGraph Python",
    "llms_txt": "https://langchain-ai.github.io/langgraph/llms.txt"
  }
]

Programmatic Usage

You can also use the server programmatically in Python:

from mcpdoc.main import create_server

# Create a server with documentation sources
server = create_server(
    [
        {
            "name": "LangGraph Python",
            "llms_txt": "https://langchain-ai.github.io/langgraph/llms.txt",
        },
        # You can add multiple documentation sources
    ],
    follow_redirects=True,
    timeout=15.0,
)

# Run the server
server.run(transport="stdio")

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "langgraph-docs-mcp" '{"command":"uvx","args":["--from","mcpdoc","mcpdoc","--urls","LangGraph:https://langchain-ai.github.io/langgraph/llms.txt","--transport","stdio","--port","8081","--host","localhost"]}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "langgraph-docs-mcp": {
            "command": "uvx",
            "args": [
                "--from",
                "mcpdoc",
                "mcpdoc",
                "--urls",
                "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt",
                "--transport",
                "stdio",
                "--port",
                "8081",
                "--host",
                "localhost"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "langgraph-docs-mcp": {
            "command": "uvx",
            "args": [
                "--from",
                "mcpdoc",
                "mcpdoc",
                "--urls",
                "LangGraph:https://langchain-ai.github.io/langgraph/llms.txt",
                "--transport",
                "stdio",
                "--port",
                "8081",
                "--host",
                "localhost"
            ]
        }
    }
}

3. Restart Claude Desktop for the changes to take effect