https://github.com/langchain-ai/mcpdoc
Expose llms-txt to IDEs for development
https://github.com/langchain-ai/mcpdoc
agents claude-code cursor ide llms llms-txt
Last synced: about 2 months ago
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Expose llms-txt to IDEs for development
- Host: GitHub
- URL: https://github.com/langchain-ai/mcpdoc
- Owner: langchain-ai
- License: mit
- Created: 2025-03-18T03:28:17.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T17:52:21.000Z (about 2 months ago)
- Last Synced: 2025-04-05T18:26:03.267Z (about 2 months ago)
- Topics: agents, claude-code, cursor, ide, llms, llms-txt
- Language: Python
- Homepage:
- Size: 153 KB
- Stars: 177
- Watchers: 4
- Forks: 25
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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.
## llms-txt
You can find llms.txt files for langgraph and langchain here:
| Library | llms.txt |
|------------------|------------------------------------------------------------------------------------------------------------|
| LangGraph Python | [https://langchain-ai.github.io/langgraph/llms.txt](https://langchain-ai.github.io/langgraph/llms.txt) |
| LangGraph JS | [https://langchain-ai.github.io/langgraphjs/llms.txt](https://langchain-ai.github.io/langgraphjs/llms.txt) |
| LangChain Python | [https://python.langchain.com/llms.txt](https://python.langchain.com/llms.txt) |
| LangChain JS | [https://js.langchain.com/llms.txt](https://js.langchain.com/llms.txt) |
## 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.
> **Note: Security and Domain Access Control**
>
> For security reasons, mcpdoc implements strict domain access controls:
>
> 1. **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.
>
> 2. **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-domains` parameter.
>
> 3. **Adding additional domains**: To allow fetching from domains beyond those automatically included:
> - Use `--allowed-domains domain1.com domain2.com` to 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.
#### (Optional) Test the MCP server locally with your `llms.txt` file(s) of choice:
```bash
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
```
* 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 LangChain:https://python.langchain.com/llms.txt",
"--transport",
"stdio"
]
}
}
}
```
* Confirm that the server is running in your `Cursor Settings/MCP` tab.
* Best practice is to then update Cursor Global (User) rules.
* Open Cursor `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.
* Ensure `agent` is selected.
Then, try an example prompt, such as:
```
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.
* Update `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:
* 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.
> [!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 `python` executable in the `uvx` command.
>
>
> Example configuration
>
> ```
> {
> "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 `rules` for global rules, so appending the following to your prompt.
```
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
```
* 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", "--urls", "LangChain:https://python.langchain.com/llms.txt"]}' -s local
```
* You will see `~/.claude.json` updated.
* Test by launching Claude Code and running to view your tools:
```
$ Claude
$ /mcp
```
> [!Note]
> Currently (3/21/25) it appears that Claude Code does not support `rules` for global rules, so appending the following to your prompt.
```
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:
* 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`.
* You can specify multiple URLs by using the `--urls` parameter multiple times.
* This is how we loaded `llms.txt` for the MCP server above.
```bash
mcpdoc --urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt --urls LangChain:https://python.langchain.com/llms.txt
```
You can also combine these methods to merge documentation sources:
```bash
mcpdoc --yaml sample_config.yaml --json sample_config.json --urls LangGraph:https://langchain-ai.github.io/langgraph/llms.txt --urls LangChain:https://python.langchain.com/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")
```