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ScreenMonitorMCP

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Enables real-time screen monitoring, UI element analysis, and predictive user behavior learning for AI assistants.

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Описание

Enables real-time screen monitoring, UI element analysis, and predictive user behavior learning for AI assistants.

README

CI License: MIT Python 3.9+ MCP Compatible

A REVOLUTIONARY Model Context Protocol (MCP) server! Gives AI real-time vision capabilities, UI intelligence, and predictive behavior learning power. This isn't just screen capture - it gives AI the power to truly "see" and understand your digital world!

🌟 WHY ScreenMonitorMCP?

  • 🔥 First & Only: Real-time continuous screen monitoring feature
  • 🧠 AI Intelligence: AI that understands UI elements and can interact with them
  • 🔮 Predictive: System that learns and predicts user behaviors
  • Proactive: Assistant that offers help before you need it
  • 🎯 Natural: AI that understands commands like "Click the save button"

🔥 REVOLUTIONARY FEATURES

🔄 Real-Time Continuous Monitoring

  • AI's Eyes Never Close: 2-5 FPS continuous screen monitoring
  • Smart Change Detection: Distinguishes between small, major, and critical changes
  • Proactive Analysis: AI automatically analyzes important changes
  • Adaptive Performance: Smart frame rate adjustment

🎯 UI Element Intelligence

  • Computer Vision UI Detection: Automatically recognizes buttons, forms, menus
  • OCR Text Extraction: Reads text from anywhere on the screen
  • Smart Click System: Natural language commands like "Click the save button"
  • Interaction Mapping: AI knows exactly where and how to interact

🧠 Predictive Intelligence

  • Behavior Learning: AI learns your usage patterns and habits
  • Intent Prediction: Predicts what you'll do next based on context
  • Proactive Help: Offers help before you ask
  • Workflow Optimization: Suggests improvements in your work patterns

🛠️ REVOLUTIONARY MCP TOOLS

🔄 Real-Time Monitoring Tools

  • start_continuous_monitoring() - Starts AI's continuous vision capability
  • stop_continuous_monitoring() - Stops continuous monitoring
  • get_monitoring_status() - Real-time status information and statistics
  • get_recent_changes() - Recently detected screen changes

🎯 UI Intelligence Tools

  • analyze_ui_elements() - Recognizes and maps all UI elements on screen
  • smart_click() - Smart clicking with natural language commands ("Click the save button")
  • extract_text_from_screen() - OCR text extraction from screen

🧠 Predictive AI Tools

  • learn_user_patterns() - Learns and analyzes user behavior patterns
  • predict_user_intent() - Predicts user intent based on current context
  • proactive_assistance() - Offers proactive help before user requests
  • record_user_action() - Records user actions and feeds learning system

📸 Traditional Tools

  • capture_and_analyze() - Screen capture and AI analysis (enhanced)
  • list_tools() - MCP standard compliant lists all tools (categorized, detailed information)

🎯 USAGE SCENARIOS

🔍 Real-Time Monitoring

# Start AI's continuous vision capability
await start_continuous_monitoring(fps=3, change_threshold=0.1)

# Check monitoring status
status = await get_monitoring_status()

# View recent changes
changes = await get_recent_changes(limit=5)

🎯 UI Intelligence

# Analyze all UI elements on screen
ui_analysis = await analyze_ui_elements()

# Smart clicking with natural language
await smart_click("Click the save button")

# Extract text from screen
text_data = await extract_text_from_screen()

🧠 Predictive AI

# Learn user behavior patterns
patterns = await learn_user_patterns()

# Predict user intent
intent = await predict_user_intent()

# Get proactive assistance
assistance = await proactive_assistance()

🚀 INSTALLATION

1. Prepare Project Files

# Navigate to project directory
cd ScreenMonitorMCP

# Install required libraries
pip install -r requirements.txt

2. Configure Environment Variables

Edit the .env file:

# Server Configuration
HOST=127.0.0.1
PORT=7777
API_KEY=your_secret_key

# AI Configuration
OPENAI_API_KEY=your_openai_api_key
OPENAI_BASE_URL=https://api.openai.com/v1
DEFAULT_OPENAI_MODEL=gpt-4o

3. Standalone Testing (Optional)

# Test the server
python main.py

# Test revolutionary features
python test_revolutionary_features.py

🔧 MCP CLIENT SETUP

Claude Desktop / MCP Client Configuration

Add the following JSON to your MCP client's configuration file:

🎯 Simple Configuration (Recommended)

{
  "mcpServers": {
    "screenMonitorMCP": {
      "command": "python",
      "args": ["/path/to/ScreenMonitorMCP/main.py"],
      "cwd": "/path/to/ScreenMonitorMCP"
    }
  }
}

🔧 Advanced Configuration

{
  "mcpServers": {
    "screenMonitorMCP": {
      "command": "python",
      "args": [
        "/path/to/ScreenMonitorMCP/main.py"
      ],
      "cwd": "/path/to/ScreenMonitorMCP",
      "env": {
        "OPENAI_API_KEY": "your-api-key-here"
      }
    }
  }
}

🛡️ Secure Configuration

{
  "mcpServers": {
    "screenMonitorMCP": {
      "command": "python",
      "args": [
        "/path/to/ScreenMonitorMCP/main.py",
        "--api-key", "your-secret-key"
      ],
      "cwd": "/path/to/ScreenMonitorMCP"
    }
  }
}

🪟 Windows Example

{
  "mcpServers": {
    "screenMonitorMCP": {
      "command": "python",
      "args": ["C:/path/to/ScreenMonitorMCP/main.py"],
      "cwd": "C:/path/to/ScreenMonitorMCP"
    }
  }
}

⚠️ Important Notes

  1. File Path: Update /path/to/ScreenMonitorMCP/main.py path according to your project directory
  2. Python Path: Make sure Python is in PATH or use full path: "C:/Python311/python.exe"
  3. Working Directory: cwd parameter is important for proper .env file reading
  4. API Keys: All settings are automatically read from .env file

🧪 USAGE EXAMPLES

🔄 Starting Real-Time Monitoring

# Start AI's continuous vision capability
result = await start_continuous_monitoring(
    fps=3,
    change_threshold=0.1,
    smart_detection=True
)

# Check monitoring status
status = await get_monitoring_status()

# View recent changes
changes = await get_recent_changes(limit=10)

# Stop monitoring
await stop_continuous_monitoring()

🎯 Using UI Intelligence

# Analyze all UI elements on screen
ui_elements = await analyze_ui_elements(
    detect_buttons=True,
    extract_text=True,
    confidence_threshold=0.7
)

# Smart clicking with natural language
await smart_click("Click the save button", dry_run=False)

# Extract text from specific region
text_data = await extract_text_from_screen(
    region={"x": 100, "y": 100, "width": 500, "height": 300}
)

🧠 Predictive Intelligence

# Learn user behavior patterns
patterns = await learn_user_patterns()

# Predict user intent
intent = await predict_user_intent(
    current_context={"current_app": "VSCode"}
)

# Get proactive assistance
assistance = await proactive_assistance()

# Record user action
await record_user_action(
    action_type="click",
    target="save_button",
    app_context="VSCode"
)

📸 Traditional Screen Capture

# Enhanced screen capture and analysis
result = await capture_and_analyze(
    capture_mode="all",
    analysis_prompt="What do you see on this screen?",
    max_tokens=500
)

# List all tools
tools = await list_tools()

🚀 REVOLUTIONARY CAPABILITIES

This MCP server gives AI the following capabilities:

  • 👁️ Continuous Vision: AI can monitor the screen non-stop
  • 🧠 Smart Understanding: Recognizes UI elements and interacts with them
  • 🔮 Future Prediction: Learns and predicts user behaviors
  • Proactive Help: Offers help before you need it
  • 🎯 Natural Interaction: Understands commands like "Click the save button"

🔧 TROUBLESHOOTING

Common Issues and Solutions

  1. Unicode/Encoding Error (Windows)

    UnicodeEncodeError: 'charmap' codec can't encode character
    

    Solution: ✅ This error is fixed! Server automatically uses UTF-8 encoding.

  2. JSON Configuration Error

    // ❌ Wrong
    {
      "command": "python",
      "args": ["path/to/main.py",]  // Trailing comma is wrong
    }
    
    // ✅ Correct
    {
      "command": "python",
      "args": ["path/to/main.py"]
    }
    
  3. Python Path Issue

    {
      "command": "C:/Python311/python.exe",  // Use full path
      "args": ["C:/path/to/ScreenMonitorMCP/main.py"]
    }
    
  4. Missing Dependencies

    cd ScreenMonitorMCP
    pip install -r requirements.txt
    
  5. OCR Issues

    # Install Tesseract (optional)
    # EasyOCR installs automatically
    
  6. MCP Connection Closed Error

    MCP error -32000: Connection closed
    

    Solution: Check file paths and add cwd parameter.

📝 LICENSE

This project is licensed under the MIT License.


🚀 Revolutionary MCP server that gives AI real "eyes"! 🔥 Next-generation AI-human interaction starts here!

from github.com/ideook/ScreenMonitorMCP

Установка ScreenMonitorMCP

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/ideook/ScreenMonitorMCP

FAQ

ScreenMonitorMCP MCP бесплатный?

Да, ScreenMonitorMCP MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для ScreenMonitorMCP?

Нет, ScreenMonitorMCP работает без API-ключей и переменных окружения.

ScreenMonitorMCP — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить ScreenMonitorMCP в Claude Desktop, Claude Code или Cursor?

Открой ScreenMonitorMCP на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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