MCP LLMS-TXT Documentation Server

The MCP LLMS-TXT Documentation Server is a specialized Model Control Protocol (MCP) server that delivers documentation directly from llms.txt files. It serves as a testbed for integrating documentation into IDEs via external tools, rather than relying solely on built-in features. While future IDEs may offer robust native support for llms.txt files, this server allows us to experiment with alternative methods, giving us full control over how documentation is retrieved and displayed.

Usage

Cursor

1. Install Cursor: https://www.cursor.com/en

2. Launch the MCP server in SSE transport.

   uvx --from mcpdoc mcpdoc \
       --urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt \ 
       --transport sse \
       --port 8081
       --host localhost
   

3. Add the mcp server to Cursor. Remember to put the URL as [host]/sse for example http://localhost:8081/sse.

Cursor needs to be in agent mode for this to work.

5. You should be able to use it within composer now.

Claude Code

1. Install Claude Code: https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview

2. Install uv. This step is required if you want to run the MCP server in using uvx command. This is generally recommended as it'll simplify all the dependency management for you.

3. Configure the MCP server with claude code

   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 user
   

4. Launch claude code

   claude code
   

   Verify that the server is running by typing /mcp in the chat window.

   > /mcp
   

5. Test it out!

   > Write a langgraph application with two agents that debate the merits of taking a shower.
   

This MCP server was only configured with LangGraph documentation, but you can add more documentation sources by adding more --urls arguments or loading it from a JSON file or a YAML file.

Command-line Interface

The mcpdoc command provides a simple CLI for launching the documentation server. You can specify documentation sources in three ways, and these can be combined:

1. Using a YAML config file:

mcpdoc --yaml sample_config.yaml

This will load the LangGraph Python documentation from the sample_config.yaml file.

2. Using a JSON config file:

mcpdoc --json sample_config.json

This will load the LangGraph Python documentation from the sample_config.json file.

3. Directly specifying llms.txt URLs with optional names:

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

URLs can be specified either as plain URLs or with optional names using the format name:url.

You can also combine these methods to merge documentation sources:

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 additional options:

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

This will load the LangGraph Python documentation with a 15-second timeout and follow any HTTP redirects if necessary.

Configuration Format

Both YAML and JSON configuration files should contain a list of documentation sources. Each source must include an llms_txt URL and can optionally include a name:

YAML Configuration Example (sample_config.yaml)

# 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 (sample_config.json)

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

Programmatic Usage

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
        # {
        #     "name": "Another Documentation",
        #     "llms_txt": "https://example.com/llms.txt",
        # },
    ],
    follow_redirects=True,
    timeout=15.0,
)

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