📌Overview

Purpose: To provide a Model Context Protocol (MCP) server that enables AI models to perform Google searches and analyze webpage content programmatically.

Overview: The Google Search MCP Server is designed to enhance the capabilities of AI assistants by integrating robust search and content extraction features. Through a seamless interface, users can conduct advanced searches and process webpage content efficiently, adhering to the MCP standards.

Key Features:

Advanced Google Search: Offers comprehensive search capabilities with filters for date, language, country, and safe search, allowing for targeted information retrieval.
Detailed Webpage Content Extraction: Facilitates the analysis of webpage content, extracting readable text while eliminating unnecessary clutter, thereby enhancing the relevance of returned information.
Batch Webpage Analysis: Allows users to analyze multiple sources simultaneously, making it ideal for comparing information or gathering extensive data on specific topics.
API Credential Management: Supports environment variable configuration for easy integration and credential management, streamlining the setup process.
MCP Compliance: Ensures smooth integration with AI assistants, supporting effective interaction between various components.

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

Clone the repository:

git clone https://github.com/your-username/google-search-mcp.git
cd google-search-mcp

Install Node.js dependencies:
```
npm install
```

Install Python dependencies:

pip install flask google-api-python-client flask-cors beautifulsoup4 trafilatura markdownify

Build the TypeScript code:
```
npm run build
```

Create a helper script to start the Python servers (Windows example):

@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:

Environment Variables (Recommended):
- Set GOOGLE_API_KEY and GOOGLE_SEARCH_ENGINE_ID in your environment
- The server will automatically use these values

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

Method 1: Start Python Servers Separately (Recommended)

First, start the Python servers using the helper script:
```
start-python-servers.cmd
```

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

1. google_search

Search Google and return relevant results from the web.

{
  "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.

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

3. extract_multiple_webpages

Extract and analyze content from multiple webpages in a single request.

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

Example Usage

Basic Search

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:

Getting Google API Credentials

Go to the Google Cloud Console: https://console.cloud.google.com/
Create a new project or select an existing one
Enable the Custom Search API
Create API credentials (API Key)
Go to the Custom Search Engine page: https://programmablesearchengine.google.com/about/
Create a new search engine and get your Search Engine ID
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:

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

License

MIT

Google-Search-MCP-Server