Python MCP Server - Documentation Search
LocalAbout Python MCP Server - Documentation Search
A Python MCP server that searches documentation for LangChain, LlamaIndex, and OpenAI using Serper API, then extracts text from relevant web pages. It is for users who want to retrieve library documentation through an MCP client.
Key features
- 01Documentation search for three libraries
- 02Web search through Serper API
- 03Text extraction from documentation pages
- 04Dummy tool for server checks
Use cases
- 01Finding LangChain documentation for a query
- 02Retrieving LlamaIndex documentation content
- 03Searching OpenAI documentation
MCP support
Exposes get_docs and dummy_tool to MCP clients. get_docs searches a selected library's documentation and returns relevant page text.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMEPython MCP Server - Documentation Search

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.
git clone <repository-url>
cd <repository-directory>
pip install -r requirements.txt
Or, if you're not using a requirements.txt file:
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.
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:
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:
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.
dotenv and logging
- Loads the Serper API Key from the
.envfile. - 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
.envfile. 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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.