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Google Search Tool

by iwanghc
GitHub
Python
MIT License
3
  • Apple
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Google Search Tool

A Playwright-based Python tool that bypasses search engine anti-scraping mechanisms to execute Google searches and extract results. It can be used directly as a command-line tool or as a Model Context Protocol (MCP) server to provide real-time search capabilities to AI assistants like Claude.

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Key Features

  • Local SERP API Alternative: No need to rely on paid search engine results API services, all searches are executed locally

  • Advanced Anti-Bot Detection Bypass Techniques:

    • Intelligent browser fingerprint management that simulates real user behavior

    • Automatic saving and restoration of browser state to reduce verification frequency

    • Smart headless/headed mode switching, automatically switching to headed mode when verification is needed

    • Randomization of device and locale settings to reduce detection risk

  • Raw HTML Retrieval: Ability to fetch the raw HTML of search result pages (with CSS and JavaScript removed) for analysis and debugging when Google's page structure changes

  • Page Screenshot: Automatically captures and saves a full-page screenshot when saving HTML content

  • MCP Server Integration: Provides real-time search capabilities to AI assistants like Claude without requiring additional API keys

  • Completely Open Source and Free: All code is open source with no usage restrictions, freely customizable and extensible

  • Python Native: Built with Python for better performance and easier deployment

Technical Features

  • Developed with Python 3.8+, providing excellent performance and wide compatibility

  • Browser automation based on Playwright, supporting multiple browser engines

  • Command-line parameter support for search keywords

  • MCP server support for AI assistant integration

  • Returns search results with title, link, and snippet

  • Option to retrieve raw HTML of search result pages for analysis

  • JSON format output

  • Support for both headless and headed modes (for debugging)

  • Detailed logging output

  • Robust error handling

  • Browser state saving and restoration to effectively avoid anti-bot detection

  • Anti-bot protection mechanisms at multiple levels

Installation

# Install from source git clone https://github.com/iwanghc/mcp_web_search.git cd mcp_web_search # Install Python dependencies pip install -r requirements.txt # Install Playwright browsers playwright install chromium

Usage

Command Line Tool

# Direct command line usage python cli.py "search keywords" # Using command line options python cli.py --limit 5 --timeout 30000 "search keywords" # Get raw HTML of search result page python cli.py --get-html "search keywords" # Get HTML and save to file python cli.py --get-html --save-html "search keywords"

MCP Server

# Configure model API KEY and other information in dotenv.env # USE MCP server python -m mcp_integration.client

Command Line Options

  • -l, --limit <number>: Result count limit (default: 10)

  • -t, --timeout <number>: Timeout time in milliseconds (default: 30000)

  • --no-headless: Show browser interface (for debugging)

  • --remote-debugging-port <number>: Enable remote debugging port (default: 9222)

  • --state-file <path>: Browser state file path (default: ./browser-state.json)

  • --no-save-state: Don't save browser state

  • --get-html: Get raw HTML of search result page instead of parsed results

  • --save-html: Save HTML to file (use with --get-html)

  • --html-output <path>: Specify HTML output file path (use with --get-html and --save-html)

  • -V, --version: Show version number

  • -h, --help: Show help information

Output Example

{ "query": "deepseek", "results": [ { "title": "DeepSeek", "link": "https://www.deepseek.com/", "snippet": "DeepSeek-R1 is now live and open source, rivaling OpenAI's Model o1. Available on web, app, and API. Click for details. Into ..." }, { "title": "DeepSeek", "link": "https://www.deepseek.com/", "snippet": "DeepSeek-R1 is now live and open source, rivaling OpenAI's Model o1. Available on web, app, and API. Click for details. Into ..." }, { "title": "deepseek-ai/DeepSeek-V3", "link": "https://github.com/deepseek-ai/DeepSeek-V3", "snippet": "We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token." } // More results... ] }

HTML Output Example

When using the --get-html option, the output will contain HTML content-related information:

{ "query": "playwright automation", "url": "https://www.google.com/", "originalHtmlLength": 1291733, "cleanedHtmlLength": 456789, "htmlPreview": "<!DOCTYPE html><html itemscope=\"\" itemtype=\"http://schema.org/SearchResultsPage\" lang=\"zh-CN\"><head><meta charset=\"UTF-8\"><meta content=\"dark light\" name=\"color-scheme\"><meta content=\"origin\" name=\"referrer\">..." }

If you also use the --save-html option, the output will also include the HTML saved file path:

{ "query": "playwright automation", "url": "https://www.google.com/", "originalHtmlLength": 1292241, "cleanedHtmlLength": 458976, "savedPath": "./google-search-html/playwright_automation-2025-04-06T03-30-06-852Z.html", "screenshotPath": "./google-search-html/playwright_automation-2025-04-06T03-30-06-852Z.png", "htmlPreview": "<!DOCTYPE html><html itemscope=\"\" itemtype=\"http://schema.org/SearchResultsPage\" lang=\"zh-CN\">..." }

MCP Server

This project provides Model Context Protocol (MCP) server functionality, allowing AI assistants like Claude to directly use Google search capabilities. MCP is an open protocol that enables AI assistants to securely access external tools and data.

Integration with Claude Desktop

  1. Edit Claude Desktop configuration file

    • Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

    • Windows: %APPDATA%\Claude\claude_desktop_config.json

      • Usually located at C:\Users\username\AppData\Roaming\Claude\claude_desktop_config.json

      • You can enter %APPDATA%\Claude in Windows Explorer address bar to access directly

  2. Add server configuration and restart Claude

{ "mcpServers": { "google-search": { "command": "python", "args": ["mcp_server_simple.py"] } } }

After integration, you can directly use search functionality in Claude, such as "Search for the latest AI research".

Project Structure

google-search/ ├── Core Functions/ │ ├── google_search/ │ │ ├── engine.py # Search engine implementation │ │ ├── browser_manager.py # Browser management and automation │ │ ├── search_executor.py # Search execution logic │ │ ├── html_extractor.py # HTML parsing and extraction │ │ ├── fingerprint.py # Browser fingerprint management │ │ ├── utils.py # Utility functions │ │ └── __init__.py # Package initialization │ └── cli.py # Command line interface ├── MCP Integration/ │ ├── mcp_integration/ │ │ ├── server.py # MCP server implementation │ │ ├── client.py # MCP client implementation │ │ └── __init__.py # Package initialization ├── Common Utilities/ │ ├── common/ │ │ ├── logger.py # Logging system │ │ ├── types.py # Common data types │ │ └── __init__.py # Package initialization ├── Configuration & Runtime/ │ ├── requirements.txt # Python dependencies │ ├── dotenv.env # Environment variables │ ├── browser-state.json # Browser state persistence │ ├── browser-state-fingerprint.json # Browser fingerprint data │ └── .gitignore # Git ignore rules ├── Documentation/ │ ├── README.md # English documentation │ ├── README.zh-CN.md # Chinese documentation │ └── google_search/REFACTOR_README.md # Refactoring notes ├── Development/ │ ├── .vscode/ # VS Code configuration │ └── logs/ # Application logs └── Other/ └── __pycache__/ # Python cache files

Technology Stack

  • Python 3.8+: Development language, providing excellent performance and compatibility

  • Playwright: For browser automation, supporting multiple browsers

  • MCP SDK: For implementing MCP server development tools

  • asyncio: Python's standard library for asynchronous I/O

  • aiofiles: Asynchronous file operations

Development Guide

All commands can be run in the project root directory:

# Install dependencies pip install -r requirements.txt # Install Playwright browsers playwright install chromium # Run CLI tool python cli.py "search keywords" # Start MCP server python mcp_server_simple.py # Test MCP client python mcp_client_enhanced.py

Error Handling

The tool has built-in robust error handling mechanisms:

  • Provides friendly error information when browser startup fails

  • Automatically returns error status when network connection issues occur

  • Provides detailed logs when search result parsing fails

  • Gracefully exits and returns useful information in timeout situations

Notes

General Notes

  • This tool is for learning and research purposes only

  • Please comply with Google's terms of service and policies

  • Don't send requests too frequently to avoid being blocked by Google

  • Some regions may require proxies to access Google

  • Playwright requires browser installation, which will be automatically downloaded on first use

State Files

  • State files contain browser cookies and storage data, please keep them safe

  • Using state files can effectively avoid Google's anti-bot detection and improve search success rate

MCP Server

  • When using MCP server, please ensure Claude Desktop is updated to the latest version

  • When configuring Claude Desktop, please use absolute paths pointing to MCP server files

Windows Environment Special Notes

  • In Windows environment, first run may require administrator privileges to install Playwright browsers

  • If you encounter permission issues, try running Command Prompt or PowerShell as administrator

  • Windows Firewall may block Playwright browser network connections, please allow access when prompted

  • Browser state files are saved by default in the user's home directory under .google-search-browser-state.json

  • Log files are saved in the system temporary directory under google-search-logs folder

Comparison with Commercial SERP APIs

Compared to paid search engine results API services (such as SerpAPI), this project provides the following advantages:

  • Completely Free: No API call fees required

  • Local Execution: All searches are executed locally, no dependency on third-party services

  • Privacy Protection: Search queries are not recorded by third parties

  • Customizability: Completely open source, can be modified and extended as needed

  • No Usage Restrictions: Not subject to API call count or frequency limitations

  • MCP Integration: Native support for integration with AI assistants like Claude

Anti-Bot Protection

This project implements multiple layers of anti-bot protection:

Client Level Protection

  • Request interval control with random delays

  • Timeout protection for tool calls

  • Intelligent error handling

Server Level Protection

  • Request frequency limiting

  • Random delays between requests

  • Global request counting and management

Search Level Protection

  • Browser fingerprint randomization

  • Device and locale randomization

  • Browser state management

  • Automatic CAPTCHA detection and handling

Performance

  • Response Time: Typically 5-15 seconds

  • Success Rate: 95%+ (with state files)

  • Concurrency: Supports asynchronous operations

  • Memory Usage: Optimized with cleanup after each call

  • Stability: Robust error recovery and timeout handling

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

Enables AI assistants to perform real-time Google searches with anti-bot protection. Bypasses search engine restrictions using advanced browser automation to extract search results locally without requiring paid API services.

  1. Key Features
    1. Technical Features
      1. Installation
        1. Usage
          1. Command Line Tool
          2. MCP Server
          3. MCP Server
        2. Project Structure
          1. Technology Stack
            1. Development Guide
              1. Error Handling
                1. Notes
                  1. General Notes
                  2. State Files
                  3. MCP Server
                  4. Windows Environment Special Notes
                2. Comparison with Commercial SERP APIs
                  1. Anti-Bot Protection
                    1. Client Level Protection
                    2. Server Level Protection
                    3. Search Level Protection
                  2. Performance

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