G-Search MCP

Parallel Google search server with MCP support for structured AI context.

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G-Search MCP is a server for executing parallel Google searches optimized for use with AI tools through the Model Context Protocol. It efficiently handles multiple queries at once, simulates realistic user behavior to evade detection, and returns well-structured JSON responses. The server is configurable, supports parameter tuning like search limits and timeouts, and is designed for seamless integration in AI-driven environments such as Claude Desktop.

Key Features

Parallel Google search execution for multiple keywords

Structured search results in JSON format

Automatic CAPTCHA and verification handling

Realistic user behavior simulation to avoid blocking

Configurable search parameters (limit, timeout, locale, debug mode)

Browser instance optimization with multiple tabs

Integration with Claude Desktop via MCP

Easy startup via npx or Playwright

Batch query support

Debug mode with visible browser for troubleshooting

Use Cases

Retrieving search results in parallel for multiple queries

Automating data collection for knowledge-based AI models

Supplying up-to-date web search context to AI assistants

Seamless context integration with tools like Claude Desktop

Bypassing CAPTCHA challenges during automated searches

Rapid extraction of structured information from Google

Bulk keyword research for business or academic use

Localized search result gathering for language processing

Debugging or monitoring userlike search automation workflows

Customizing web search workflows through parameterized commands

README

G-Search MCP

A powerful MCP server for Google search that enables parallel searching with multiple keywords simultaneously.

This project is modified from google-search.

🌟 Recommended: OllaMan - Powerful Ollama AI Model Manager.

Advantages

Parallel Searching: Supports searching with multiple keywords on Google simultaneously, improving search efficiency
Browser Optimization: Opens multiple tabs in a single browser instance for efficient parallel searching
Automatic Verification Handling: Intelligently detects CAPTCHA and enables visible browser mode for user verification when needed
User Behavior Simulation: Simulates real user browsing patterns to reduce the possibility of detection by search engines
Structured Data: Returns structured search results in JSON format for easy processing and analysis
Configurable Parameters: Supports various parameter configurations such as search result limits, timeout settings, locale settings, etc.

Quick Start

Run directly with npx:

bash

npx -y g-search-mcp

First time setup - install the required browser by running the following command in your terminal:

bash

npx playwright install chromium

Debug Mode

Use the --debug option to run in debug mode (showing browser window):

bash

npx -y g-search-mcp --debug

Configure MCP

Configure this MCP server in Claude Desktop:

MacOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%/Claude/claude_desktop_config.json

json

{
  "mcpServers": {
    "g-search": {
      "command": "npx",
      "args": ["-y", "g-search-mcp"]
    }
  }
}

Features

search - Execute Google searches with multiple keywords and return results
- Uses Playwright browser to perform searches
- Supports the following parameters:
  - queries: Array of search queries to execute (required parameter)
  - limit: Maximum number of results to return per query, default is 10
  - timeout: Page loading timeout in milliseconds, default is 60000 (60 seconds)
  - noSaveState: Whether to avoid saving browser state, default is false
  - locale: Locale setting for search results, default is en-US
  - debug: Whether to enable debug mode (showing browser window), overrides the --debug flag in command line

Example usage:

Use the search tool to search for "machine learning" and "artificial intelligence" on Google

Example response:

json

{
  "searches": [
    {
      "query": "machine learning",
      "results": [
        {
          "title": "What is Machine Learning? | IBM",
          "link": "https://www.ibm.com/topics/machine-learning",
          "snippet": "Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy."
        },
        ...
      ]
    },
    {
      "query": "artificial intelligence",
      "results": [
        {
          "title": "What is Artificial Intelligence (AI)? | IBM",
          "link": "https://www.ibm.com/topics/artificial-intelligence",
          "snippet": "Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind."
        },
        ...
      ]
    }
  ]
}

Usage Tips

Handling Special Website Scenarios

Adjusting Search Parameters

Search Result Quantity: For more search results:
```
Please return the top 20 search results for each keyword
```
This will set the limit: 20 parameter.
Increase Timeout Duration: For slow loading situations:
```
Please set the page loading timeout to 120 seconds
```
This will adjust the timeout parameter to 120000 milliseconds.

Locale Settings Adjustment

Change Search Region: Specify a different locale setting:
```
Please use Chinese locale (zh-CN) for searching
```
This will set the locale: "zh-CN" parameter.

Debugging and Troubleshooting

Enable Debug Mode

Dynamic Debug Activation: To display the browser window during a specific search operation:
```
Please enable debug mode for this search operation
```
This sets debug: true even if the server was started without the --debug flag.

Installation

Prerequisites

Node.js 18 or higher
NPM or Yarn

Install from Source

Clone the repository:

bash

git clone https://github.com/jae-jae/g-search-mcp.git
cd g-search-mcp

Install dependencies:

bash

npm install

Install Playwright browser:

bash

npm run install-browser

Build the server:

bash

npm run build

Development

Auto Rebuild (Development Mode)

bash

npm run watch

Using MCP Inspector for Debugging

bash

npm run inspector

Related Projects

fetcher-mcp: A powerful MCP server for fetching web page content using Playwright headless browser. Features intelligent content extraction, parallel processing, resource optimization, and more, making it an ideal tool for web content scraping.

License

Licensed under the MIT License

Star History

Repository Owner

jae-jae

User

Repository Details

Language TypeScript

Default Branch main

Size 42 KB

Contributors 1

License MIT License

MCP Verified Nov 12, 2025

Programming Languages

TypeScript

97.9%

JavaScript

2.1%

Topics

google-search mcp playwright

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G-Search MCP

Key Features

Use Cases

README

G-Search MCP

Advantages

Quick Start

Debug Mode

Configure MCP

Features

Usage Tips

Handling Special Website Scenarios

Adjusting Search Parameters

Locale Settings Adjustment

Debugging and Troubleshooting

Enable Debug Mode

Installation

Prerequisites

Install from Source

Development

Auto Rebuild (Development Mode)

Using MCP Inspector for Debugging

Related Projects

License

Star History

Repository Owner

Repository Details

Programming Languages

Tags

Topics

Related MCPs