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 json { "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) 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 json { "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: bash git clone <repository-url> cd mcp-website-tool 2. Install dependencies: bash pip install -e ".[dev]" 3. Install Playwright browsers: bash playwright install chromium ### Running Tests bash 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 bash 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 bash 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. --- Made with ❤️ for the AI community