🔍 Web Analyzer MCP

A powerful MCP (Model Context Protocol) server for intelligent web content analysis and summarization. Built with FastMCP, this server provides smart web scraping, content extraction, and AI-powered question-answering capabilities.

✨ Features

🎯 Core Tools

1. url_to_markdown - Extract and summarize key web page content

   • Analyzes content importance using custom algorithms
   • Removes ads, navigation, and irrelevant content
   • Keeps only essential information (tables, images, key text)
   • Outputs structured markdown optimized for analysis

2. web_content_qna - AI-powered Q&A about web content

   • Extracts relevant content sections from web pages
   • Uses intelligent chunking and relevance matching
   • Answers questions using OpenAI GPT models

🚀 Key Features

• Smart Content Ranking: Algorithm-based content importance scoring
• Essential Content Only: Removes clutter, keeps what matters
• Multi-IDE Support: Works with Claude Desktop, Cursor, VS Code, PyCharm
• Flexible Models: Choose from GPT-3.5, GPT-4, GPT-4 Turbo, or GPT-5

📦 Installation

Prerequisites

• uv (Python package manager)
• Chrome/Chromium browser (for Selenium)
• OpenAI API key (for Q&A functionality)

🚀 Quick Start with uv (Recommended)

# Clone the repository
git clone https://github.com/kimdonghwi94/web-analyzer-mcp.git
cd web-analyzer-mcp

# Run directly with uv (auto-installs dependencies)
uv run mcp-webanalyzer

Installing via Smithery

To install web-analyzer-mcp for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @kimdonghwi94/web-analyzer-mcp --client claude

IDE/Editor Integration

Add to your Claude Desktop_config.json file. See Claude Desktop MCP documentation for more details.

{
  "mcpServers": {
    "web-analyzer": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/web-analyzer-mcp",
        "run", 
        "mcp-webanalyzer"
      ],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "OPENAI_MODEL": "gpt-4"
      }
    }
  }
}

Add the server using Claude Code CLI:

claude mcp add web-analyzer -e OPENAI_API_KEY=your_api_key_here -e OPENAI_MODEL=gpt-4 -- uv --directory /path/to/web-analyzer-mcp run mcp-webanalyzer

Add to your Cursor settings (File > Preferences > Settings > Extensions > MCP):

{
  "mcpServers": {
    "web-analyzer": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/web-analyzer-mcp",
        "run", 
        "mcp-webanalyzer"
      ],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "OPENAI_MODEL": "gpt-4"
      }
    }
  }
}

See JetBrains AI Assistant Documentation for more details.

1. In JetBrains IDEs go to Settings → Tools → AI Assistant → Model Context Protocol (MCP)
2. Click + Add
3. Click on Command in the top-left corner of the dialog and select the As JSON option from the list
4. Add this configuration and click OK:

{
  "mcpServers": {
    "web-analyzer": {
      "command": "uv",
      "args": [
        "--directory",
        "/path/to/web-analyzer-mcp",
        "run", 
        "mcp-webanalyzer"
      ],
      "env": {
        "OPENAI_API_KEY": "your_openai_api_key_here",
        "OPENAI_MODEL": "gpt-4"
      }
    }
  }
}

🎛️ Tool Descriptions

url_to_markdown

Converts web pages to clean markdown format with essential content extraction.

Parameters:

• url (string): The web page URL to analyze

Returns: Clean markdown content with structured data preservation

web_content_qna

Answers questions about web page content using intelligent content analysis.

Parameters:

• url (string): The web page URL to analyze
• question (string): Question about the page content

Returns: AI-generated answer based on page content

🏗️ Architecture

Content Extraction Pipeline

1. URL Validation - Ensures proper URL format
2. HTML Fetching - Uses Selenium for dynamic content
3. Content Parsing - BeautifulSoup for HTML processing
4. Element Scoring - Custom algorithm ranks content importance
5. Content Filtering - Removes duplicates and low-value content
6. Markdown Conversion - Structured output generation

Q&A Processing Pipeline

1. Content Chunking - Intelligent text segmentation
2. Relevance Scoring - Matches content to questions
3. Context Selection - Picks most relevant chunks
4. Answer Generation - OpenAI GPT integration

🏗️ Project Structure

web-analyzer-mcp/
├── web_analyzer_mcp/          # Main Python package
│   ├── __init__.py           # Package initialization
│   ├── server.py             # FastMCP server with tools
│   ├── web_extractor.py      # Web content extraction engine
│   └── rag_processor.py      # RAG-based Q&A processor
├── scripts/                   # Build and utility scripts
│   └── build.js              # Node.js build script
├── README.md                 # English documentation
├── README.ko.md              # Korean documentation
├── package.json              # npm configuration and scripts
├── pyproject.toml            # Python package configuration
├── .env.example              # Environment variables template
└── dist-info.json            # Build information (generated)

🛠️ Development

Modern Development with uv

# Clone repository
git clone https://github.com/kimdonghwi94/web-analyzer-mcp.git
cd web-analyzer-mcp

# Development commands
uv run mcp-webanalyzer     # Start development server
uv run python -m pytest   # Run tests
uv run ruff check .        # Lint code
uv run ruff format .       # Format code
uv sync                    # Sync dependencies

# Install development dependencies
uv add --dev pytest ruff mypy

# Create production build
npm run build

Alternative: Traditional Python Development

# Setup Python environment (if not using uv)
pip install -e .[dev]

# Development commands
python -m web_analyzer_mcp.server  # Start server
python -m pytest tests/            # Run tests
python -m ruff check .             # Lint code
python -m ruff format .            # Format code
python -m mypy web_analyzer_mcp/   # Type checking

🤝 Contributing

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

📋 Roadmap

• Support for more content types (PDFs, videos)
• Multi-language content extraction
• Custom extraction rules
• Caching for frequently accessed content
• Webhook support for real-time updates

⚠️ Limitations

• Requires Chrome/Chromium for JavaScript-heavy sites
• OpenAI API key needed for Q&A functionality
• Rate limited to prevent abuse
• Some sites may block automated access

📄 License

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

🙋‍♂️ Support

• Create an issue for bug reports or feature requests
• Contribute to discussions in the GitHub repository
• Check the documentation for detailed guides

🌟 Acknowledgments

• Built with FastMCP framework
• Inspired by HTMLRAG techniques for web content processing
• Thanks to the MCP community for feedback and contributions

Made with ❤️ for the MCP community