Google Search MCP Server

This repository contains a Model Context Protocol (MCP) server that provides a tool-based interface to Google's Custom Search Engine (CSE) API. It is designed for use with MCP-compatible clients, such as those found in IDEs like Visual Studio Code, allowing developers and models to perform Google searches programmatically.

Features

• Google Search Integration: Perform Google searches using the CSE API.
• Expanded Search Parameters: Support for a wide range of search parameters, including dateRestrict, siteSearch, fileType, and more.
• Flexible Configuration: Configure the server via environment variables or a .env file.
• User-Friendly Tools: Provides a clear google_search tool with support for various search parameters.
• Conversational Outputs: Returns search results in a conversational format, making it easy for clients to use the results.
• Standalone and Asynchronous: The server runs as a standalone script and uses an asynchronous architecture for better performance.

Prerequisites

Before running the server, you need to have Python 3.12+ and uv installed. You will also need a Google API key and a Programmable Search Engine (CSE) ID. You will also need Node.js and npx to run the MCP Inspector tool for testing.

Installation

1. Clone the repository.

   # Use gh cli to clone repository
   gh repo clone fvanevski/google_search_mcp
   
   # Alternateively, use git to clone repository
   # git clone https://github.com/fvanevski/google_search_mcp.git
   
   # Enter the repository directory
   cd google_search_mcp
   

2. Create a virtual environment and install the required dependencies:

   # Create a virtual environment
   uv venv
   
   # Activate the virtual environment
   source .venv/bin/activate
   
   # Install the dependencies
   uv sync
   

Running and Testing the Server

The MCP server is a command-line application that communicates over standard I/O. To use it, a client (like an IDE, a coding agent, or an inspector tool) must launch the server process.

Running for Diagnostics

You can try to run the script directly from your terminal to see if it starts without errors. This is a quick way to validate your Python environment and the script's basic syntax.

python google_search_mcp.py

However, the server will simply start and wait for input, so you won't be able to interact with it directly from your terminal.

Testing with MCP Inspector

The recommended way to test the server interactively is with MCP Inspector. It runs as a command-line tool and provides an interactive shell for sending requests to your server.

1. Launch the Inspector: You can run the inspector without a permanent installation using npx. The inspector will launch your MCP server script for you. From your project directory, run:

   # Run the inspector with `uv run --with <dependencies> -- python3 <script>`
   npx @modelcontextprotocol/inspector uv run --with requests,python-dotenv,pydantic,mcp -- python3 google_search_mcp.py
   

   Even if you have your virtual environment active, the python command as executed by the inspector will not correctly point to the interpreter with the necessary dependencies, thus we use uv instead with the --with flag.

2. Interact with the Server: Once the inspector starts, you can click the "Connect" button to establish a session with your server. You can then use ommands like list_tools and call_tool to interact with it.

   Example session:

   # List all available tools
   > list_tools
   
   # Call the 'google_search' tool with arguments
   > call_tool google_search '''{"query": "AI news", "dateRestrict": "d7"}'''
   

   This provides a reliable way to test all the tools and verify that the server is working as expected.

Configuration

The server is configured using environment variables or a .env file in your project's root directory.

Environment Variables

• GOOGLE_API_KEY: Your Google API key.
• GOOGLE_CSE_ID: Your Custom Search Engine (CSE) ID.

Example .env file:

GOOGLE_API_KEY=your-secret-api-key
GOOGLE_CSE_ID=your-cse-id

Usage with an MCP Client (VS Code Example)

You can connect to this server from any standard MCP client. Here’s how to do it in a VS Code environment that supports MCP:

1. Configure Your MCP Client: In your IDE's MCP client settings (e.g., in mcp.json for VS Code), configure a new MCP server that points to the script.

   Example mcp.json entry for VS Code:

   {
     "servers": {
       "google_search": {
         "command": "uv",
         "args": [
           "run",
           "python",
           "google_search_mcp.py"
         ],
         "cwd": "/path/to/your/project"
       }
     }
   }
   

   Note: Replace /path/to/your/project with the actual path to the project directory.

2. Use the Tools: Once connected, you can use the exposed tools in your chat or agent interactions with natural language queries or structured calls. For example, to perform a Google search for recent PDF documents about machine learning, you could send the following structured tool call:

   {
     "tool": "google_search",
     "arguments": {
       "query": "machine learning",
       "dateRestrict": "m1",
       "fileType": "pdf"
     }
   }
   

   The server will execute the query and return the search results.