README.md•8.8 kB
# MCP LLMS-TXT Documentation Server
## Overview
[llms.txt](https://llmstxt.org/) 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](https://github.com/modelcontextprotocol) offers a way for developers to have *full control* over tools used by these applications. Here, we create [an open source MCP server](https://github.com/modelcontextprotocol) 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.
## Quickstart
#### Install uv
* Please see [official uv docs](https://docs.astral.sh/uv/getting-started/installation/#installation-methods) for other ways to install `uv`.
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
#### Choose an `llms.txt` file to use.
* For example, [here's](https://langchain-ai.github.io/langgraph/llms.txt) the LangGraph `llms.txt` file.
#### (Optional) Test the MCP server locally with your `llms.txt` file of choice:
```bash
uvx --from mcpdoc mcpdoc \
--urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt \
--transport sse \
--port 8082 \
--host localhost
```
* This should run at: http://localhost:8082
* Run [MCP inspector](https://modelcontextprotocol.io/docs/tools/inspector) and connect to the running server:
```bash
npx @modelcontextprotocol/inspector
```
* Here, you can test the `tool` calls.
#### Connect to Cursor
* Open `Cursor Settings` and `MCP` tab.
* This will open the `~/.cursor/mcp.json` file.
* Paste the following into the file (we use the `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",
"--transport",
"stdio",
"--port",
"8081",
"--host",
"localhost"
]
}
}
}
```
* Confirm that the server is running in your `Cursor Settings/MCP` tab.
* `CMD+L` (on Mac) to open chat.
* Ensure `agent` is selected.
Then, try an example prompt, such as:
```
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?
```
### Connect to Windsurf
* Open Cascade with `CMD+L` (on Mac).
* Click `Configure MCP` to open the config file, `~/.codeium/windsurf/mcp_config.json`.
* Update with `langgraph-docs-mcp` as noted above.
* `CMD+L` (on Mac) to open Cascade and refresh MCP servers.
* Available MCP servers will be listed, showing `langgraph-docs-mcp` as connected.
Then, try the example prompt:
* It will perform your tool calls.
### Connect to Claude Desktop
* Open `Settings/Developer` to update `~/Library/Application\ Support/Claude/claude_desktop_config.json`.
* Update with `langgraph-docs-mcp` as noted above.
* Restart Claude Desktop app.
* 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](https://docs.anthropic.com/en/docs/agents-and-tools/claude-code/overview), 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"]}' -s local
```
* You will see `~/.claude.json` updated.
* Test by launching Claude Code and running to view your tools:
```
$ Claude
$ /mcp
```
Then, try the example prompt:
* It will ask to approve tool calls.
## 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:
* This will load the LangGraph Python documentation from the `sample_config.yaml` file in this repo.
```bash
mcpdoc --yaml sample_config.yaml
```
2. Using a JSON config file:
* This will load the LangGraph Python documentation from the `sample_config.json` file in this repo.
```bash
mcpdoc --json sample_config.json
```
3. Directly specifying llms.txt URLs with optional names:
* URLs can be specified either as plain URLs or with optional names using the format `name:url`.
* This is how we loaded `llms.txt` for the MCP server above.
```bash
mcpdoc --urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt
```
You can also combine these methods to merge documentation sources:
```bash
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:
```bash
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)
```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)
```json
[
{
"name": "LangGraph Python",
"llms_txt": "https://langchain-ai.github.io/langgraph/llms.txt"
}
]
```
## Programmatic Usage
```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
# {
# "name": "Another Documentation",
# "llms_txt": "https://example.com/llms.txt",
# },
],
follow_redirects=True,
timeout=15.0,
)
# Run the server
server.run(transport="stdio")
```