https://github.com/sagacious-satadru/documentation-mcp
An MCP server that can work with Claude desktop to fetch documentation from langchain, llama-index, and OpenAI.
https://github.com/sagacious-satadru/documentation-mcp
Last synced: 3 months ago
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An MCP server that can work with Claude desktop to fetch documentation from langchain, llama-index, and OpenAI.
- Host: GitHub
- URL: https://github.com/sagacious-satadru/documentation-mcp
- Owner: sagacious-satadru
- Created: 2025-04-12T15:48:43.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-04-12T16:06:07.000Z (6 months ago)
- Last Synced: 2025-06-17T13:05:31.716Z (4 months ago)
- Language: Python
- Size: 1.25 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Documentation MCP Server 📚🔍
A Model Context Protocol (MCP) server that enables Claude to search and access documentation from popular libraries like LangChain, LlamaIndex, and OpenAI directly within conversations.
## What is MCP? 🤔
MCP (Model Context Protocol) is an open protocol that standardizes how applications provide context to Large Language Models. Think of it as a universal connector that lets AI assistants like Claude access external data sources and tools.
## Features ✨
- **Documentation Search Tool**: Search through documentation of popular AI libraries
- **Supported Libraries**:
- [LangChain](https://python.langchain.com/docs) 🔗
- [LlamaIndex](https://docs.llamaindex.ai/en/stable) 🦙
- [OpenAI](https://platform.openai.com/docs) 🤖
- **Smart Extraction**: Intelligently parses HTML content to extract the most relevant information
- **Configurable Results**: Limit the amount of text returned based on your needs
## How It Works 🛠️
1. The server uses the Serper API to perform Google searches with site-specific queries
2. It fetches the content from the search results
3. BeautifulSoup extracts the most relevant text from main content areas
4. Claude can access this information through the `get_docs` tool
## System Requirements 🖥️
- Python 3.11 or higher
- `uv` package manager
- A Serper API key
## Setup Instructions 🚀
### 1. Install uv Package Manager
```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
```
### 2. Clone and Set Up the Project
```bash
# Clone or download the project
cd documentation
# Create and activate virtual environment
uv venv
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate
# Install dependencies
uv pip install -e .
```
### 3. Configure the Serper API Key
Create a `.env` file in the project directory with your Serper API key:
```
SERPER_API_KEY=your_serper_api_key_here
```
You can get a Serper API key by signing up at [serper.dev](https://serper.dev).
### 4. Configure Claude Desktop
Edit your Claude Desktop configuration file at:
- Windows: `/C:/Users/[Your Username]/AppData/Roaming/Claude/claude_desktop_config.json`
- macOS: `~/Library/Application Support/Claude/claude_desktop_config.json`
Add the following to the `mcpServers` section:
```json
"documentation": {
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/YOUR/documentation",
"run",
"main.py"
]
}
```
Replace `/ABSOLUTE/PATH/TO/YOUR/documentation` with the absolute path to your project directory.
### 5. Restart Claude Desktop
Close and reopen Claude Desktop to apply the new configuration.
## Using the Documentation Tool 🧩
Once connected, you can ask Claude to use the documentation tool:
> "Can you look up information about vector stores in LangChain documentation?"
Claude will use the `get_docs` tool to search for relevant information and provide you with documentation excerpts.
## Tool Parameters 📋
The `get_docs` tool accepts the following parameters:
- `query`: The search term (e.g., "vector stores", "embedding models")
- `library`: Which library to search (langchain, llama-index, or openai)
- `max_chars`: Maximum characters to return (default: 1000)
## Troubleshooting 🛠️
- **Claude can't find the server**: Verify the path in `/C:/Users/fcbsa/AppData/Roaming/Claude/claude_desktop_config.json` is correct
- **Search returns no results**: Check your Serper API key and internet connection
- **Timeout errors**: The server might be experiencing connectivity issues or rate limits
## License 📜
This project is provided as an educational example of MCP server implementation.
## Acknowledgements 🙏
- Built using the [MCP SDK](https://github.com/modelcontextprotocol)
- Powered by [Serper API](https://serper.dev) for Google search integration
- Uses [BeautifulSoup4](https://www.crummy.com/software/BeautifulSoup/) for HTML parsing
- Inspired by the growing MCP community
---
*This MCP server enhances Claude's capabilities by providing direct access to documentation resources. Explore, learn, and build better AI applications with contextual knowledge from the docs!*