# Directory Structure
```
├── .env
├── main.py
├── pyproject.toml
├── README.md
└── uv.lock
```
# Files
--------------------------------------------------------------------------------
/.env:
--------------------------------------------------------------------------------
```
SERPER_API_KEY=e297b5743f3404f2ab1262b5a827e6c41000b1b0
```
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
```markdown
# 🤖 Claude AI Documentation Assistant 📚
<div align="center">
*A powerful MCP server that supercharges Claude with documentation search capabilities*
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](http://makeapullrequest.com)
</div>
## ✨ Features
- 🔍 **Smart Documentation Search** - Search across multiple AI/ML library documentation
- 🧠 **Claude Integration** - Seamless connection with Claude's advanced reasoning capabilities
- 🌐 **Intelligent Web Search** - Leverages Serper API for targeted documentation lookup
- 💨 **Fast Response Times** - Optimized for quick retrieval and processing
- 🧩 **Extendable Architecture** - Easily add more documentation sources
## 📋 Prerequisites
- 🐍 Python 3.8 or higher
- 🔑 Claude Pro subscription
- 🔐 Serper API key ([Get one here](https://serper.dev))
- 💻 Claude Desktop application
## 🚀 Quick Start
### 1️⃣ Installation
```bash
# Clone the repository
git clone https://github.com/your-username/claude-docs-assistant.git
cd claude-docs-assistant
# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
```
### 2️⃣ Configuration
Create a `.env` file in the project root with your API keys:
```
SERPER_API_KEY=your_serper_api_key_here
```
### 3️⃣ Start the MCP Server
```bash
python main.py
```
You should see output indicating the server is running and waiting for Claude to connect.
### 4️⃣ Connect Claude Desktop App
1. 📱 Open the Claude Desktop App
2. ⚙️ Click on your profile icon and select "Settings"
3. 🧰 Navigate to the "Tools" section
4. ➕ Click "Add Tool"
5. 🔗 Select "Connect to a local tool"
6. 🖥️ Follow the prompts to connect to your running MCP server
7. ✅ Confirm the connection is successful
## 🎮 Using Your Claude Documentation Assistant
Once connected, you can start asking Claude questions that will trigger the documentation search. For example:
```
Could you explain how to use FAISS with LangChain? Please search the langchain documentation to help me.
```
Claude will automatically use your MCP server to:
1. 🔍 Search for relevant documentation
2. 📥 Retrieve the content
3. 🧠 Process and explain the information
## 🔧 Under the Hood
### 📄 Code Structure
```
claude-docs-assistant/
├── main.py # MCP server implementation
├── requirements.txt # Project dependencies
├── .env # Environment variables (API keys)
└── README.md # This documentation
```
### 🔌 Supported Libraries
The assistant currently supports searching documentation for:
- 🦜 **LangChain**: `python.langchain.com/docs`
- 🦙 **LlamaIndex**: `docs.llamaindex.ai/en/stable`
- 🧠 **OpenAI**: `platform.openai.com/docs`
### 🧩 How It Works
1. 📡 The MCP server exposes a `get_docs` tool to Claude
2. 🔍 When invoked, the tool searches for documentation using Serper API
3. 📚 Results are scraped for their content
4. 🔄 Content is returned to Claude for analysis and explanation
## 🛠️ Advanced Configuration
### Adding New Documentation Sources
Extend the `docs_urls` dictionary in `main.py`:
```python
docs_urls = {
"langchain": "python.langchain.com/docs",
"llama-index": "docs.llamaindex.ai/en/stable",
"openai": "platform.openai.com/docs",
"huggingface": "huggingface.co/docs", # Add new documentation sources
"tensorflow": "www.tensorflow.org/api_docs",
}
```
### Customizing Search Behavior
Modify the `search_web` function to adjust the number of results:
```python
payload = json.dumps({"q": query, "num": 5}) # Increase from default 2
```
## 🔍 Troubleshooting
### Common Issues
- **🚫 "Connection refused" error**: Ensure the MCP server is running before connecting Claude
- **⏱️ Timeout errors**: Check your internet connection or increase the timeout value
- **🔒 API key issues**: Verify your Serper API key is correct in the `.env` file
### Debugging Tips
Add more detailed logging by modifying the main.py file:
```python
import logging
logging.basicConfig(level=logging.DEBUG)
```
## 📈 Performance Optimization
- ⚡ For faster response times, consider caching frequently accessed documentation
- 🧠 Limit the amount of text returned to Claude to avoid token limitations
- 🌐 Use more specific queries to get more relevant documentation
## 🤝 Contributing
Contributions are welcome! Here's how you can help:
1. 🍴 Fork the repository
2. 🌿 Create a feature branch (`git checkout -b feature/amazing-feature`)
3. 💾 Commit your changes (`git commit -m 'Add some amazing feature'`)
4. 📤 Push to the branch (`git push origin feature/amazing-feature`)
5. 🔍 Open a Pull Request
## 📜 License
This project is licensed under the MIT License - see the LICENSE file for details.
## 🙏 Acknowledgements
- [Anthropic](https://www.anthropic.com/) for creating Claude
- [Serper.dev](https://serper.dev) for their search API
- All the open-source libraries that make this project possible
---
<div align="center">
Made with ❤️ for Claude enthusiasts
</div>
```
--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
```toml
[project]
name = "documentation"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.13"
dependencies = [
"httpx>=0.28.1",
"mcp[cli]>=1.5.0",
]
```
--------------------------------------------------------------------------------
/main.py:
--------------------------------------------------------------------------------
```python
from mcp.server.fastmcp import FastMCP
from dotenv import load_dotenv
import httpx
import json
import os
from bs4 import BeautifulSoup
load_dotenv()
mcp= FastMCP("docs")
USER_AGENT = "docs-app/1.0"
SERPER_URL = "https://google.serper.dev/search
docs_urls = {
"langchain": "python.langchain.com/docs",
"llama-index": "docs.llamaindex.ai/en/stable",
"openai": "platform.openai.com/docs",
}
async def search_web(query: str) -> dict | None:
payload = json.dumps({"q": query, "num": 2})
headers = {
"X-API-KEY": os.getenv("SERPER_API_KEY"),
"Content-Type": "application/json",
}
async with httpx.AsyncClient() as client:
try:
response = await client.post(
SERPER_URL, headers=headers, data=payload, timeout=30.0
)
response.raise_for_status()
return response.json()
except httpx.TimeoutException:
return {"organic": []}
async def fetch_url(url: str):
async with httpx.AsyncClient() as client:
try:
response = await client.get(url, timeout=30.0)
soup = BeautifulSoup(response.text, "html.parser")
text = soup.get_text()
return text
except httpx.TimeoutException:
return "Timeout error"
@mcp.tool()
async def get_docs(query: str, library: str):
"""
Search the latest docs for a given query and library.
Supports langchain, openai, and llama-index.
Args:
query: The query to search for (e.g. "Chroma DB")
library: The library to search in (e.g. "langchain")
Returns:
Text from the docs
"""
if library not in docs_urls:
raise ValueError(f"Library {library} not supported by this tool")
query = f"site:{docs_urls[library]} {query}"
results = await search_web(query)
if len(results["organic"]) == 0:
return "No results found"
text = ""
for result in results["organic"]:
text += await fetch_url(result["link"])
return text
if __name__ == "__main__":
mcp.run(transport="stdio")
```