aiwebsearcher 0.1.1

A Model Context Protocol (MCP) server providing AI-powered Baidu search with intelligent reranking and web content extraction

Search MCP Server

A powerful Model Context Protocol (MCP) server providing AI-enhanced Baidu search with intelligent reranking and comprehensive web content extraction capabilities.

✨ Features

• 🔍 Baidu Search Integration: Fast and reliable search results from Baidu
• 🤖 AI-Powered Reranking: Uses multiple AI agents (Qwen) to intelligently rerank search results by relevance
• 📄 Web Content Extraction: Extract clean, readable text from web pages with pagination support
• 🎯 Batch Processing: Extract content from multiple URLs simultaneously
• 🌐 MCP Standard: Fully compliant with Model Context Protocol for seamless integration

🚀 Quick Start

Prerequisites

• Python 3.10 or higher
• uv (recommended) or pip
• DashScope API key (for AI search features)

Installation

Using uv (Recommended)

# Clone the repository
git clone https://github.com/Vist233/Google-Search-Tool.git
cd search-mcp

# Install with uv
uv pip install -e .

Using pip

pip install -e .

Environment Setup

Create a .env file or set environment variables for AI features:

export DASHSCOPE_API_KEY="your-api-key-here"

📖 Usage

As an MCP Server

Add to your MCP client configuration (e.g., Claude Desktop):

For macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

For Windows: %APPDATA%/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "aiwebsearcher": {
      "command": "uvx",
      "args": [
        "aiwebsearcher"
      ]
    }
  }
}

Note: API key is read from environment variable DASHSCOPE_API_KEY. Set it before running:

# macOS/Linux
export DASHSCOPE_API_KEY="your-api-key-here"

# Windows (PowerShell)
$env:DASHSCOPE_API_KEY="your-api-key-here"

Standalone Testing

# Install the package
pip install aiwebsearcher

# Set API key
export DASHSCOPE_API_KEY="your-key"

# Run the server
aiwebsearcher

🛠️ Available Tools

1. search_baidu

Execute basic Baidu search and return structured results.

Parameters:

• query (str): Search keyword
• max_results (int, optional): Maximum results to return (default: 5)
• language (str, optional): Search language (default: "zh")

Returns: JSON string with title, url, and abstract for each result.

Example:

{
  "query": "人工智能发展现状",
  "max_results": 5
}

2. AI_search_baidu

AI-enhanced search with intelligent reranking and content extraction. Takes ~3x longer but provides higher quality, ranked results with full page content.

Parameters:

• query (str): Search keyword
• max_results (int, optional): Initial results to fetch (default: 5, recommended 5+)
• language (str, optional): Search language (default: "zh")

Returns: JSON string with rank, title, url, and Content (full page text) for each result.

Example:

{
  "query": "AI发展趋势 2025",
  "max_results": 12
}

3. extractTextFromUrl

Extract clean, readable text from a single webpage.

Parameters:

• url (str): Target webpage URL
• follow_pagination (bool, optional): Follow rel="next" links (default: true)
• pagination_limit (int, optional): Max pagination depth (default: 3)
• timeout (float, optional): HTTP timeout in seconds (default: 10.0)
• user_agent (str, optional): Custom User-Agent header
• regular_expressions (list[str], optional): Regex patterns to filter text

Returns: Extracted text content as string.

4. extractTextFromUrls

Extract text from multiple webpages in batch.

Parameters: Same as extractTextFromUrl, plus:

• urls (list[str]): List of target URLs

Returns: Combined text from all URLs, separated by double newlines.

🏗️ Project Structure

search-mcp/
├── searcher/
│   └── src/
│       ├── server.py              # MCP server entry point
│       ├── FetchPage/
│       │   └── fetchWeb.py        # Web content extraction
│       ├── WebSearch/
│       │   ├── baiduSearchTool.py # Baidu search implementation
│       │   └── SearchAgent.py     # AI agent definitions (legacy)
│       └── useAI2Search/
│           └── SearchAgent.py     # AI-powered search orchestration
├── tests/                         # Test files
├── pyproject.toml                # Project configuration
├── requirements.txt              # Dependencies
└── README.md                     # This file

🔧 Development

Install Development Dependencies

uv pip install -e ".[dev]"

Run Tests

pytest

Code Formatting

# Format with black
black searcher/

# Lint with ruff
ruff check searcher/

📝 Configuration

MCP Client Configuration Examples

Minimal configuration:

{
  "mcpServers": {
    "search": {
      "command": "python",
      "args": ["server.py"],
      "cwd": "/path/to/search-mcp/searcher/src"
    }
  }
}

With uv for dependency isolation:

{
  "mcpServers": {
    "search": {
      "command": "uv",
      "args": ["--directory", "/path/to/search-mcp/searcher/src", "run", "python", "server.py"]
    }
  }
}

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with FastMCP
• AI models powered by Agno and DashScope
• Search powered by baidusearch
• Content extraction using trafilatura

📮 Contact

• GitHub: @Vist233
• Repository: Google-Search-Tool

⚠️ Disclaimer

This tool is for educational and research purposes. Please respect website terms of service and rate limits when scraping content.