MCP.tools Actor – Turn any site into an AI tool

Transform any website into an AI tool with MCP (Model Context Protocol) tools. This Apify Actor automatically extracts interactive elements from web pages and generates MCP-compatible tool definitions that can be used with Cursor, Claude, and other AI assistants.

🚀 Features

• 🎯 Interactive Element Extraction: Automatically finds buttons, inputs, dropdowns, and links
• 🍪 Cookie Banner Removal: Removes OneTrust, Cookiebot, and other common cookie banners
• 🔧 MCP Tools Generation: Creates MCP-compatible tool definitions with safe names and descriptions
• 📸 Visual Preview: Generates beautiful preview pages with one-click integration buttons
• 🛡️ Error Handling: Automatic screenshot capture and error logging
• 📊 Structured Output: Clean JSON format ready for MCP integration

📋 Example Input

{
  "url": "https://example.com",
  "cookies": [
    {"name": "session_id", "value": "abc123"}
  ],
  "removeBanners": true,
  "maxActions": 50
}

Input Parameters

• url (required): The website URL to process
• cookies (optional): List of cookies to set for authenticated pages
• removeBanners (default: true): Automatically remove cookie consent banners
• maxActions (default: 50, min: 5, max: 200): Maximum number of interactive elements to extract

📤 Example Output

The actor generates three outputs:

1. MCP Tools JSON (mcp-{id}.json)

{
  "tools": [
    {
      "name": "button_submit_form",
      "description": "Click the Submit Form button",
      "input_schema": {
        "type": "object",
        "properties": {
          "selector": {
            "type": "string",
            "description": "CSS selector for the Submit Form button",
            "default": "#submit-btn"
          }
        },
        "required": ["selector"]
      }
    },
    {
      "name": "input_username",
      "description": "Enter text in the Enter username input field",
      "input_schema": {
        "type": "object",
        "properties": {
          "selector": {
            "type": "string",
            "description": "CSS selector for the Enter username input field",
            "default": "#username"
          }
        },
        "required": ["selector"]
      }
    }
  ]
}

2. Dataset Record

{
  "mcpJsonUrl": "https://api.apify.com/v2/key-value-stores/.../records/mcp-abc123.json",
  "previewUrl": "https://api.apify.com/v2/key-value-stores/.../records/preview-abc123.html",
  "screenshotUrl": "https://api.apify.com/v2/key-value-stores/.../records/screenshot-abc123.png",
  "toolCount": 12,
  "url": "https://example.com",
  "runId": "abc123",
  "actionsCount": 12
}

3. Preview HTML Page

A beautiful preview page with:

• 📊 Statistics dashboard (tools count, actions count, run ID)
• 🚀 One-click buttons for Cursor and Claude integration
• 📋 Visual action cards showing all extracted elements
• 🔧 Pretty-printed MCP JSON for easy inspection

💰 Pricing

$0.03 per run - Affordable pricing for transforming websites into AI tools.

🛠️ Installation

Local Development

1. Clone the repository:

git clone <repository-url>
cd mcp-website-tool

2. Install dependencies:

$pip install -e ".[dev]"

3. Install Playwright browsers:

$playwright install chromium

Running Tests

$pytest

📖 Usage

Via Apify Platform

1. Go to Apify Console
2. Find the MCP.tools Actor
3. Configure input with your target URL
4. Run and get your MCP tools JSON!

Via API

curl -X POST "https://api.apify.com/v2/acts/YOUR_ACTOR_ID/run-sync" \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "url": "https://example.com",
    "maxActions": 50
  }'

🏗️ Project Structure

├── src/
│   ├── main.py              # Main Actor entry point
│   ├── types.py             # Pydantic models for validation
│   ├── browser.py           # Playwright browser management
│   ├── extractor.py          # Interactive element extraction
│   ├── mcp_generator.py      # MCP tools generation
│   └── utils.py             # Utility functions
├── tests/                   # Comprehensive test suite
├── input_schema.json        # Apify input schema
├── apify.json               # Apify actor configuration
└── README.md                # This file

🔧 Development

Code Quality

black src tests
ruff check src tests
mypy src
pytest

Test-Driven Development

This project follows TDD principles:

1. Write failing tests (red)
2. Implement minimal code (green)
3. Refactor while keeping tests passing

📦 Dependencies

• apify: Apify SDK for actor development
• playwright: Browser automation framework
• pydantic: Data validation using Python type annotations
• structlog: Structured logging

📝 License

MIT License - see LICENSE file for details

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch
3. Write tests first (TDD)
4. Implement the feature
5. Ensure all tests pass
6. Submit a pull request

📞 Support

For issues and questions, please open an issue on the repository.

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Made with ❤️ for the AI community