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    Document Search Mcp Server

    MCP (Model Context Protocol) server that allows users to search for documentation from popular libraries such as LangChain, LlamaIndex, and OpenAI using the Serper API. The server fetches search results and extracts the relevant documentation from the web using HTTP requests and BeautifulSoup.

    0 stars
    Python
    Updated Jun 9, 2025
    claude-ai
    langchain
    mcp-server
    mcp-tools
    openai

    Table of Contents

    • Features
    • Requirements
    • Installation
    • Step 1: Install Python Dependencies
    • Step 2: Set Up Environment Variables
    • Step 3: Run the Server
    • Usage
    • Example Query
    • Available Libraries
    • Code Overview
    • Troubleshooting
    • Contributor

    Table of Contents

    • Features
    • Requirements
    • Installation
    • Step 1: Install Python Dependencies
    • Step 2: Set Up Environment Variables
    • Step 3: Run the Server
    • Usage
    • Example Query
    • Available Libraries
    • Code Overview
    • Troubleshooting
    • Contributor

    Documentation

    Python MCP Server - Documentation Search

    mcp_server.jpg

    This is a simple MCP (Minimal Command Protocol) server that allows users to search for documentation from popular

    libraries such as LangChain, LlamaIndex, and OpenAI using the Serper API. The server fetches search

    results and extracts the relevant documentation from the web using HTTP requests and BeautifulSoup.

    Features

    • Supports searching documentation for LangChain, LlamaIndex, and OpenAI.
    • Uses Serper API to perform web searches.
    • Extracts and returns the text from the relevant documentation pages.
    • Can be used interactively through MCP protocol.

    Requirements

    • Python 3.12 or higher.
    • Serper API Key for performing web searches.
    • MCP library for the server and tool execution.
    • BeautifulSoup4 and httpx for HTTP requests and HTML parsing.

    Installation

    Step 1: Install Python Dependencies

    To set up the project, clone this repository and install the required dependencies.

    bash
    git clone 
    cd 
    pip install -r requirements.txt

    Or, if you're not using a requirements.txt file:

    bash
    pip install httpx beautifulsoup4 mcp python-dotenv

    Step 2: Set Up Environment Variables

    Create a .env file in the root directory of the project with your Serper API Key.

    bash
    SERPER_API_KEY=your-serper-api-key-here

    Make sure the .env file is loaded using the dotenv package.

    Step 3: Run the Server

    Once the environment is set up, run the server using the following command:

    bash
    uv run main.py

    The server will start and wait for input.

    Usage

    Once the server is running, you can use it to query the latest documentation for specific libraries. Here’s an example

    of how to query the tool:

    Example Query

    Start your server with ``uv run main.py``.

    In another terminal or from within an interactive MCP client, invoke the get_docs tool to search for documentation:

    bash
    get_docs("Chroma DB", "langchain")

    This will search for "Chroma DB" in the LangChain documentation and return the relevant content.

    Available Libraries

    • LangChain: Documentation at langchain.com
    • LlamaIndex: Documentation at llamaindex.ai
    • OpenAI: Documentation at platform.openai.com

    Code Overview

    main.py```
    - FastMCP: Initializes the MCP server.
    - Tools: The server has two tools:
    - dummy_tool: A simple tool that confirms the server is up and running.
    - get_docs: Fetches the latest documentation for a given query and library (LangChain, LlamaIndex, OpenAI).
    - search_web(): Handles searching using the Serper API.
    - fetch_url(): Fetches a URL and extracts the text content using BeautifulSoup.
    • Loads the Serper API Key from the ``.env`` file.
    • Provides debug-level logging for better visibility into server actions and potential issues.`````

    Troubleshooting

    • Missing API Key: Make sure the Serper API Key is set correctly in the ``.env`` file. You’ll get an error if it's

    missing.

    • Timeout Issues: If a search or URL fetch times out, try increasing the timeout values or checking the network

    connectivity.

    • Package Installation Issues: Ensure all dependencies are installed and the correct Python environment is activated.

    Contributor

    Udayan Sawant

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