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Google Search MCP Server

by mixelpixx
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local-only server

The server can only run on the client’s local machine because it depends on local resources.

Integrations

  • Provides Google search capabilities to AI models through an MCP server interface, allowing for advanced search queries with filtering options for date, language, country, and safe search

  • Integrates with Google Cloud Platform for API credentials and Custom Search capabilities needed to power the Google search functionality

Built For use with Cline + VS Code!

Google Search MCP Server

An MCP (Model Context Protocol) server that provides Google search capabilities and webpage content analysis tools. This server enables AI models to perform Google searches and analyze webpage content programmatically.

Features

  • Advanced Google Search with filtering options (date, language, country, safe search)
  • Detailed webpage content extraction and analysis
  • Batch webpage analysis for comparing multiple sources
  • Environment variable support for API credentials
  • Comprehensive error handling and user feedback
  • MCP-compliant interface for seamless integration with AI assistants

Prerequisites

  • Node.js (v16 or higher)
  • Python (v3.8 or higher)
  • Google Cloud Platform account
  • Custom Search Engine ID
  • Google API Key

Installation

  1. Clone the repository:
    git clone https://github.com/your-username/google-search-mcp.git cd google-search-mcp
  2. Install Node.js dependencies:
    npm install
  3. Install Python dependencies:
    pip install flask google-api-python-client flask-cors beautifulsoup4 trafilatura markdownify
  4. Build the TypeScript code:
    npm run build
  5. Create a helper script to start the Python servers (Windows example):
    # Create start-python-servers.cmd @echo off echo Starting Python servers for Google Search MCP... REM Start Python search server start "Google Search API" cmd /k "python google_search.py" REM Start Python link viewer start "Link Viewer" cmd /k "python link_view.py" echo Python servers started. You can close this window.

Configuration

API Credentials

You can provide Google API credentials in two ways:

  1. Environment Variables (Recommended):
    • Set GOOGLE_API_KEY and GOOGLE_SEARCH_ENGINE_ID in your environment
    • The server will automatically use these values
  2. Configuration File:
    • Create an api-keys.json file in the root directory:
    { "api_key": "your-google-api-key", "search_engine_id": "your-custom-search-engine-id" }

MCP Settings Configuration

Add the server configuration to your MCP settings file:

For Cline (VS Code Extension)

File location: %APPDATA%\Code\User\globalStorage\saoudrizwan.claude-dev\settings\cline_mcp_settings.json

{ "mcpServers": { "google-search": { "command": "C:\\Program Files\\nodejs\\node.exe", "args": ["C:\\path\\to\\google-search-mcp\\dist\\google-search.js"], "cwd": "C:\\path\\to\\google-search-mcp", "env": { "GOOGLE_API_KEY": "your-google-api-key", "GOOGLE_SEARCH_ENGINE_ID": "your-custom-search-engine-id" }, "disabled": false, "autoApprove": [] } } }

For Claude Desktop App

File location: %APPDATA%\Claude\claude_desktop_config.json

{ "mcpServers": { "google-search": { "command": "C:\\Program Files\\nodejs\\node.exe", "args": ["C:\\path\\to\\google-search-mcp\\dist\\google-search.js"], "cwd": "C:\\path\\to\\google-search-mcp", "env": { "GOOGLE_API_KEY": "your-google-api-key", "GOOGLE_SEARCH_ENGINE_ID": "your-custom-search-engine-id" }, "disabled": false, "autoApprove": [] } } }

Running the Server

  1. First, start the Python servers using the helper script:
    start-python-servers.cmd
  2. Configure the MCP settings to run only the Node.js server:
    { "command": "C:\\Program Files\\nodejs\\node.exe", "args": ["C:\\path\\to\\google-search-mcp\\dist\\google-search.js"] }

Method 2: All-in-One Script

Start both the TypeScript and Python servers with a single command:

npm run start:all

Available Tools

Search Google and return relevant results from the web. This tool finds web pages, articles, and information on specific topics using Google's search engine.

{ "name": "google_search", "arguments": { "query": "your search query", "num_results": 5, // optional, default: 5, max: 10 "date_restrict": "w1", // optional, restrict to past day (d1), week (w1), month (m1), year (y1) "language": "en", // optional, ISO 639-1 language code (en, es, fr, de, ja, etc.) "country": "us", // optional, ISO 3166-1 alpha-2 country code (us, uk, ca, au, etc.) "safe_search": "medium" // optional, safe search level: "off", "medium", "high" } }

2. extract_webpage_content

Extract and analyze content from a webpage, converting it to readable text. This tool fetches the main content while removing ads, navigation elements, and other clutter.

{ "name": "extract_webpage_content", "arguments": { "url": "https://example.com" } }

3. extract_multiple_webpages

Extract and analyze content from multiple webpages in a single request. Ideal for comparing information across different sources or gathering comprehensive information on a topic.

{ "name": "extract_multiple_webpages", "arguments": { "urls": [ "https://example1.com", "https://example2.com" ] } }

Example Usage

Here are some examples of how to use the Google Search MCP tools:

Search for information about artificial intelligence

Advanced Search with Filters

Search for recent news about climate change from the past week in Spanish

Content Extraction

Extract the content from https://example.com/article

Multiple Content Comparison

Compare information from these websites: - https://site1.com/topic - https://site2.com/topic - https://site3.com/topic

Getting Google API Credentials

  1. Go to the Google Cloud Console
  2. Create a new project or select an existing one
  3. Enable the Custom Search API
  4. Create API credentials (API Key)
  5. Go to the Custom Search Engine page
  6. Create a new search engine and get your Search Engine ID
  7. Add these credentials to your api-keys.json file

Error Handling

The server provides detailed error messages for:

  • Missing or invalid API credentials
  • Failed search requests
  • Invalid webpage URLs
  • Network connectivity issues

Architecture

The server consists of two main components:

  1. TypeScript MCP Server: Handles MCP protocol communication and provides the tool interface
  2. Python Flask Server: Manages Google API interactions and webpage content analysis

License

MIT

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An MCP (Model Context Protocol) server that provides Google search capabilities and webpage content analysis tools. This server enables AI models to perform Google searches and analyze webpage content programmatically.

  1. Google Search MCP Server
    1. Features
    2. Prerequisites
    3. Installation
    4. Configuration
    5. Running the Server
    6. Available Tools
    7. Example Usage
    8. Getting Google API Credentials
    9. Error Handling
    10. Architecture
    11. License

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