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: 3 months ago
JSON representation
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 (4 months ago)
- Default Branch: main
- Last Pushed: 2025-04-05T17:52:21.000Z (3 months ago)
- Last Synced: 2025-04-05T18:26:03.267Z (3 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
Awesome Lists containing this project
- awesome - langchain-ai/mcpdoc - Expose llms-txt to IDEs for development (Python)
- awesome-mcp-registry - ❌ mcpdoc
- awesome-mcp-registry - ❌ mcpdoc
- mcp-index - Documentation Server - Provides a user-defined list of llms.txt files and fetches documents from URLs within those files for auditing tool calls and enhancing context retrieval for LLM interactions. Connects with various IDEs and applications to improve the development experience while ensuring transparency and control. (Task and Project Management)
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")
```