webdocs-mcp-server

📚 WebDocs MCP Server

AI-Powered Documentation Search & Retrieval System
A Model Context Protocol (MCP) server that fetches real-time documentation from popular libraries and frameworks using web search.

🌟 Features

• 🔍 Real-time Documentation Search - Fetches the latest docs from official sources
• 🚀 Multiple Library Support - Langchain, Llama-Index, OpenAI, UV, and Qubrid
• 🧹 Clean Content Extraction - Removes HTML noise and returns readable text
• 🔗 Source Attribution - Every piece of content includes its source URL
• ⚡ Fast & Async - Built with async/await for optimal performance
• 🤖 MCP Compatible - Works seamlessly with Claude Desktop and other MCP clients

Claude Desktop Usage Demo Screenshots

📦 Supported Libraries

Library	Documentation Site
🦜 Langchain	python.langchain.com/docs
🦙 Llama-Index	docs.llamaindex.ai/en/stable
🤖 OpenAI	platform.openai.com/docs
📦 UV	docs.astral.sh/uv
🎯 Qubrid	docs.qubrid.com

🛠️ Installation

Quick Start (Recommended - NPM)

The easiest way to use this MCP server is via npx:

npx -y webdocs-mcp-server

This requires:

• Node.js 16+ installed
• UV package manager (installation guide)

Manual Installation (For Development)

Prerequisites

• Python 3.10 or higher
• UV package manager

Setup

• Clone the repository

  git clone <your-repo-url>
  cd Docu_MCP
  

• Install dependencies

  uv sync
  

• Set up environment variables

  Create a .env file in the project root:

  SERPER_API_KEY=your_serper_api_key_here
  GROQ_API_KEY=your_groq_api_key_here
  

🔑 API Keys Setup

1. Serper API Key (Required)

The Serper API is used for web search functionality.

• Visit serper.dev
• Sign up for a free account
• Navigate to your dashboard
• Copy your API key
• Add it to your .env file

2. Groq API Key (Optional)

Currently reserved for future LLM integration features.

• Visit console.groq.com
• Sign up and get your API key
• Add it to your .env file

🚀 Usage

Option 1: Using the MCP Client (Python)

Test the server directly with the included client:

uv run mcp_client.py

Example output:

Available Tools: ['get_docs']
[Documentation content with sources...]

You can customize the query in mcp_client.py:

query = "How to setup ComfyUI AI ML Template?"
library = "qubrid"
res = await session.call_tool(
    "get_docs",
    arguments={"user_query": query, "library": library}
)

Option 2: Using Claude Desktop

Step 1: Configure Claude Desktop

Open your Claude Desktop config file:

• Windows: %APPDATA%\Claude\claude_desktop_config.json
• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

🚀 Recommended: NPM Installation (Remote)

Add this configuration to use the published npm package:

{
  "mcpServers": {
    "WebDocs": {
      "command": "npx",
      "args": ["-y", "webdocs-mcp-server"],
      "env": {
        "SERPER_API_KEY": "your_serper_api_key_here",
        "GROQ_API_KEY": "your_groq_api_key_here"
      }
    }
  }
}

💻 Alternative: Local Development

If you're developing locally, use this configuration:

{
  "mcpServers": {
    "WebDocs": {
      "command": "C:\\Users\\YOUR_USERNAME\\.local\\bin\\uv.EXE",
      "args": ["--directory", "D:\\Path\\To\\Docu_MCP", "run", "mcp_server.py"],
      "env": {
        "SERPER_API_KEY": "your_serper_api_key_here",
        "GROQ_API_KEY": "your_groq_api_key_here"
      }
    }
  }
}

⚠️ Important:

• For NPM method: Just add your API keys
• For local method: Replace YOUR_USERNAME and D:\\Path\\To\\Docu_MCP with your actual paths!

Step 2: Restart Claude Desktop

• Completely close Claude Desktop
• Reopen Claude Desktop
• The WebDocs tool should now be available

Step 3: Use the Tool

In Claude Desktop, you can ask:

"Use the WebDocs tool to search for how to create a ReAct agent in Langchain"

Claude will automatically call the get_docs tool with the appropriate parameters.

Option 3: Using MCP Inspector (Debugging)

The MCP Inspector is a powerful tool for testing and debugging your MCP server.

Start the Inspector

npx @modelcontextprotocol/inspector uv --directory "D:\AbhiiiMan Codes\Docu_MCP" run mcp_server.py

This will:

• Start the MCP server
• Launch a web interface at http://localhost:5173
• Allow you to interactively test the server

Using the Inspector

• Open your browser to the provided URL
• Click "Connect" to establish connection
• Navigate to "Tools" tab
• Select get_docs tool
• Fill in the parameters:
  • user_query: Your search query
  • library: One of langchain, llama-index, openai, uv, or qubrid
• Click "Run Tool" to test

Troubleshooting

If port is already in use:

# Find and kill the process using port 6277
netstat -ano | findstr :6277
taskkill /PID <process_id> /F

🔧 Available Tools

get_docs

Searches and retrieves documentation content from supported libraries.

Parameters:

• user_query (string, required): The search query
  • Example: "How to use Langchain with OpenAI"
• library (string, required): The library to search
  • Options: langchain, llama-index, openai, uv, qubrid

Returns:

• Text content from documentation with source URLs

Example:

await session.call_tool(
    "get_docs",
    arguments={
        "user_query": "vector store integration",
        "library": "langchain"
    }
)

📁 Project Structure

Docu_MCP/
├── 📄 mcp_server.py          # Main MCP server implementation
├── 📄 mcp_client.py          # Test client for the server
├── 📄 constants.py           # Configuration and constants
├── 📄 utils.py               # Utility functions (HTML cleaning)
├── 📄 test_server.py         # Server launch test script
├── 📄 .env                   # Environment variables (create this)
├── 📄 pyproject.toml         # Project dependencies
└── 📄 README.md              # This file

🔍 How It Works

graph LR
    A[User Query] --> B[MCP Server]
    B --> C[Serper API]
    C --> D[Google Search]
    D --> E[Documentation URLs]
    E --> F[Fetch Content]
    F --> G[Clean HTML]
    G --> H[Return Text]
    H --> I[User/Claude]

• Query Construction: Combines library domain with user query
• Web Search: Uses Serper API to search Google
• Content Fetching: Retrieves raw HTML from documentation pages
• Content Cleaning: Extracts readable text using Trafilatura
• Response Formation: Formats content with source attribution

🐛 Debugging & Testing

Test Server Launch

Run the test script to verify configuration:

uv run test_server.py

Expected output:

✅ Server process started successfully!
✅ Server is running and accepting connections!

Common Issues

Issue	Solution
🔴 PORT IS IN USE	Kill process on port 6277 or use different port
🔴 SERPER_API_KEY not found	Check .env file exists and contains valid key
🔴 program not found	Use full path to uv.EXE in Claude config

Enable Debug Logging

Add to your .env:

LOG_LEVEL=DEBUG

🎯 Example Use Cases

1. Learning New Framework

Query: "Getting started with vector stores"
Library: "langchain"
→ Returns: Setup guides, installation steps, basic examples

2. Troubleshooting

Query: "Error handling in async queries"
Library: "llama-index"
→ Returns: Error handling patterns, best practices

3. API Reference

Query: "Chat completion parameters"
Library: "openai"
→ Returns: Parameter documentation, examples, limits

4. Tool Setup

Query: "Installing UV on Windows"
Library: "uv"
→ Returns: Installation guide, configuration steps

🤝 Contributing

Contributions are welcome! Here's how you can help:

• Add More Libraries: Update constants.py with new documentation sources
• Improve Content Cleaning: Enhance the HTML extraction in utils.py
• Add Features: Implement caching, rate limiting, or semantic search
• Fix Bugs: Report issues or submit pull requests

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• FastMCP - MCP server framework
• Serper - Web search API
• Trafilatura - Web content extraction
• Model Context Protocol - MCP specification

📧 Support

Having issues? Here's how to get help:

• 📖 Check this README thoroughly
• 🔍 Use MCP Inspector for debugging
• 💬 Open an issue on GitHub

Built with ❤️ by AbhiiiMan

⭐ Star this repo if you find it helpful!