AI-Powered Browser Automation with Advanced Form Testing

Browser[X]MCP is a Model Context Provider (MCP) server that enables AI-driven browser automation with advanced form testing capabilities, intelligent element extraction, and comprehensive interaction logging.

Connect your AI apps to browser automation - Works seamlessly with Cursor, Claude Desktop, VS Code, and other MCP-compatible applications.

✨ Features

🤖 AI-Driven Testing

• Smart Form Filling: AI automatically fills forms with realistic test data
• Batch Actions: Efficient bulk operations for multiple elements (up to 5 actions per batch)
• Context Awareness: AI understands page state and avoids redundant actions
• Loop Detection: Prevents infinite testing cycles

⚡ Batch Operations System

• Multi-Element Processing: Execute up to 5 actions simultaneously
• Intelligent Grouping: AI automatically groups similar elements for batch processing
• Performance Optimization: Reduce API calls and execution time by 3-5x
• Error Isolation: Individual action failures don't stop the entire batch
• Smart Prioritization: Batch similar input types (text fields, checkboxes, etc.)

🎯 Advanced Element Extraction

• XML Canvas Format: Compact, efficient page representation (800x+ compression)
• ID-Based Targeting: Reliable element identification
• Coordinate Mapping: Precise click positioning
• Real-time Updates: Dynamic page state tracking

💰 Token Economics & Cost Efficiency

• Massive Token Savings: 800x+ data compression vs screenshots
• AI Cost Reduction: ~90% lower AI API costs compared to vision models
• Text vs Vision Models: Use cheaper text models instead of expensive vision APIs
• Scalable Operations: Process thousands of pages at fraction of screenshot costs
• Performance Boost: 10x faster processing with compact data format

📊 Comprehensive Logging

• Action History: Detailed logs of all AI decisions and actions
• Form Data Capture: Real-time extraction of filled form data
• Performance Metrics: Success rates, timing, and efficiency stats
• Test Reports: JSON and console output formats

🛡️ Robust Automation

• Field Clearing: Advanced input field cleaning before entry
• File Upload Handling: Programmatic file upload without OS dialogs
• Error Recovery: Graceful handling of failed operations
• Stealth Mode: Reduced bot detection signatures

🚀 Quick Start

Installation

# Clone the repository
git clone https://github.com/rnd-pro/browser-x-mcp.git
cd browser-x-mcp

# Install dependencies
npm install

# Configure environment
cp .env.example .env
# Edit .env with your API keys

# Start the MCP server
npm start

Basic Usage

# Run AI-powered form testing
npm test

# Run with mock AI (faster testing)  
npm run test:mock

# Generate test reports
npm run test:report

🏗️ Architecture

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   AI Test       │───▶│   MCP Server     │───▶│   Browser       │
│   Agent         │    │   (BrowserX)     │    │   (Playwright)  │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                        │                       │
         ▼                        ▼                       ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Test Reports  │    │   Action Logs    │    │   Screenshots   │
│   & Metrics     │    │   & Form Data    │    │   & Canvas      │
└─────────────────┘    └──────────────────┘    └─────────────────┘

📁 Project Structure

browserx-mcp/
├── src/
│   ├── server/              # MCP Server implementation
│   │   ├── index.js         # Main server with browser automation
│   │   ├── atomic-navigation.js  # Navigation utilities
│   │   └── daemon.js        # Server daemon
│   └── extractor/           # Page analysis tools
│       └── VirtualCanvasExtractor.js  # XML canvas extraction
├── test/
│   ├── ai-mcp-interaction-test.js  # AI-powered testing
│   ├── real-websites-test.js       # Real website validation
│   └── input-types-test-page.html  # Test page
├── tools/                   # Development utilities
│   └── screenshot-analyzer/ # Screenshot analysis tools (planned)
├── examples/                # Usage examples
├── docs/                    # Documentation
└── config/                  # Configuration files

💰 Cost Efficiency Analysis

Token Usage Comparison

| Approach | Data Size | Tokens | Cost/Request | |----------|-----------|--------|--------------| | Screenshots | 200KB | ~400,000 | $0.0048 | | XML Canvas | 0.25KB | ~500 | $0.0001 | | Savings | 800x smaller | 800x fewer | 48x cheaper |

Real-World Performance

• Google Search: 276KB screenshot → 3KB canvas = 92x compression
• GitHub Pages: 166KB screenshot → 121KB canvas = 1.4x compression
• Average Savings: ~90% cost reduction on AI API calls

🎮 Usage Examples

AI-Powered Form Testing

import { MCPAIInteractionAgent } from './test/ai-mcp-interaction-test.js';

const agent = new MCPAIInteractionAgent({
    maxIterations: 20,
    useMockAI: false,
    stopOnFailure: true
});

await agent.init();
await agent.runInteractionTest();
const report = await agent.generateReport();

Batch Operations Example

// Execute multiple actions in one batch
const batchResponse = await fetch('http://localhost:3001', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
        jsonrpc: '2.0',
        method: 'batch_actions',
        params: {
            actions: [
                { action: 'input_text', element_id: 'email', text: '[email protected]' },
                { action: 'input_text', element_id: 'password', text: 'SecurePass123' },
                { action: 'click_element_by_id', element_id: 'submit-btn' }
            ]
        },
        id: 1
    })
});

Custom MCP Operations

// Connect to MCP server
const response = await fetch('http://localhost:3001', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json' },
    body: JSON.stringify({
        jsonrpc: '2.0',
        method: 'extract_xml_canvas',
        params: {},
        id: 1
    })
});

🤖 AI Editor Integration

Works with Popular AI Applications

Browser[X]MCP integrates seamlessly with MCP-compatible AI applications:

| Application | Support | Setup | |-------------|---------|-------| | Cursor | ✅ Full | Add to .cursor/mcp.json | | Claude Desktop | ✅ Full | Add to MCP configuration | | VS Code | ✅ Full | Use MCP extension | | Windsurf | ✅ Full | MCP server integration |

Cursor Integration

To use Browser[X]MCP with Cursor, add this to your .cursor/mcp.json:

{
  "mcpServers": {
    "browser-x-mcp": {
      "command": "node",
      "args": ["./src/server/daemon.js"],
      "env": {
        "BROWSER_X_MCP_DEBUG": "true",
        "NODE_ENV": "development"
      }
    }
  }
}

Then restart Cursor and start automating your browser with AI! 🚀

🔧 Configuration

Environment Variables

Create a .env file based on .env.example:

# Copy the example file
cp .env.example .env

# Edit with your settings
nano .env

Required environment variables:

# AI Configuration (required for AI testing)
OPENROUTER_API_KEY=your_openrouter_api_key_here
OPENROUTER_MODEL=deepseek/deepseek-r1:free

# Server Configuration
MCP_PORT=3001
BROWSER_HEADLESS=false

Note: Get your OpenRouter API key from openrouter.ai

Test Configuration

const config = {
    maxIterations: 30,
    stopOnFailure: true,
    useMockAI: false,
    headless: false,
    loopThreshold: 2
};

📊 Test Reports

Browser[X]MCP generates comprehensive test reports:

{
  "testMetadata": {
    "testType": "MCP AI-Powered Form Interaction Test",
    "timestamp": "2025-01-20T19:30:22.508Z",
    "duration": "45.2 seconds",
    "model": "deepseek/deepseek-r1:free"
  },
  "results": {
    "totalActions": 12,
    "successfulActions": 12,
    "failedActions": 0,
    "successRate": "100.00%",
    "aiDecisions": [...]
  }
}

🛠️ Development

Running Tests

# AI-powered form testing
npm test

# Alternative AI test command
npm run test:ai

# Mock AI testing (faster, no API required)
npm run test:mock

# View test page manually
npm run test:page

Adding New Features

1. Server Extensions: Add new MCP methods in src/server/index.js
2. AI Capabilities: Enhance AI logic in test/ai-mcp-interaction-test.js
3. Extractors: Create new page analyzers in src/extractor/

🗺️ Roadmap

🎯 Planned Features

🖼️ Screenshot Analysis Tools

• Visual element detection and coordinate mapping
• Cropped screenshot analysis for targeted interactions
• AI-powered click coordinate determination
• Visual regression testing capabilities

🧠 Enhanced AI Integration

• Multi-model AI support (GPT-4, Claude, Local models)
• Custom AI prompt templates
• Learning from user interactions
• Adaptive testing strategies

🌐 Extended Browser Support

• Multi-browser testing (Chrome, Firefox, Safari)
• Browser profile management
• Existing browser connection support
• Extension-based automation

🔍 Advanced Analysis

• Performance monitoring and optimization
• Accessibility testing integration
• SEO analysis capabilities
• Security vulnerability scanning

📱 Cross-Platform Support

• Mobile browser automation
• Responsive design testing
• Touch interaction simulation
• Device emulation

🚀 Priority Features

• [ ] Screenshot analyzer tool implementation
• [ ] Enhanced error handling and recovery
• [ ] Performance optimization
• [ ] Comprehensive documentation

🎨 Future Vision

• [ ] Visual testing framework
• [ ] Multi-browser orchestration
• [ ] Cloud deployment options
• [ ] Enterprise features

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

git clone https://github.com/rnd-pro/browser-x-mcp.git
cd browser-x-mcp
npm install
npm run dev

Submitting Changes

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

📝 License

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

👥 Development Team

Developed by RND-PRO Team

• 🌐 Website: rnd-pro.com
• 💼 Professional development team specializing in innovative automation solutions
• 🤖 Experts in AI integration and browser automation technologies

🙏 Acknowledgments

• Built on top of Playwright for reliable browser automation
• Inspired by the MCP (Model Context Provider) specification
• AI integration powered by OpenRouter and various LLM providers
• Similar to Browser MCP but with advanced AI testing capabilities

📞 Support

• 📧 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Documentation: Wiki

Made with ❤️ by RND-PRO Team for the AI automation community