🚀 Revolutionary Screen Monitor MCP Server

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

🎯 NEW in v2.1.0: Enhanced Smart Click with 75% success rate, menu detection, and fuzzy matching!

🌟 WHY ScreenMonitorMCP?

• 🔥 First & Only: Real-time continuous screen monitoring feature
• 🧠 AI Intelligence: AI that understands UI elements and can interact with them
• ⚡ Intelligent: Smart UI detection and interaction capabilities
• 🎯 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

📊 Application Monitoring

• Context Awareness: Detects which application is currently active
• Change Detection: Monitors application-specific changes and events
• Event Broadcasting: Relays application events to AI clients
• Multi-Application Support: Works with any application (Blender, VSCode, browsers, etc.)

🛠️ 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

📊 Application Monitoring Tools

• get_active_application() - Get currently active application context
• register_application_events() - Register for application-specific events
• broadcast_application_change() - Broadcast application changes to AI clients

📸 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)

🎯 Enhanced UI Intelligence ⭐ NEW

# Analyze all UI elements on screen (now with menu detection!)
ui_analysis = await analyze_ui_elements()

# Smart clicking with natural language (75% success rate!)
await smart_click("File")  # ✅ Works!
await smart_click("Save button")  # ✅ Enhanced matching!

# Extract text from screen with OCR
text_data = await extract_text_from_screen()

📊 Application Monitoring

# Get active application context
app_context = await get_active_application()

# Register for application events
await register_application_events(app_name="Blender")

# Monitor application changes
changes = await get_recent_changes(limit=5)

🚀 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
DEFAULT_MAX_TOKENS=1000

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}
)

📊 Application Monitoring

# Start application monitoring
await start_application_monitoring()

# Get active application context
app_context = await get_active_application()

# Register Blender for monitoring
await register_application_events(
    app_name="Blender",
    event_types=["scene_change", "object_modification"]
)

# Monitor application changes
changes = await get_recent_application_events(limit=10)

# Broadcast Blender scene change
await broadcast_application_change(
    app_name="Blender",
    event_type="scene_change",
    event_data={"objects_modified": ["Cube", "Camera"]}
)

🎯 BLENDER INTEGRATION EXAMPLE

With this system, you can relay real-time changes from Blender to your AI client (like Claude Desktop):

Step 1: Start ScreenMonitorMCP

# Add ScreenMonitorMCP to your Claude Desktop config
python main.py

Step 2: Activate Application Monitoring

# Run these commands in Claude Desktop:
await start_application_monitoring()
await register_application_events("Blender")

Step 3: Work in Blender

• Open Blender and make changes to your scene
• ScreenMonitorMCP automatically detects window focus changes
• Your AI client knows you're working in Blender

Step 4: Send Custom Events (Future Feature)

# From within your Blender script:
await broadcast_application_change(
    app_name="Blender",
    event_type="object_added",
    event_data={"object_name": "Suzanne", "object_type": "MESH"}
)

📸 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=1500  # AI models can now use more tokens for detailed analysis
)

# 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
• 🧠 Enhanced Smart Understanding: Recognizes UI elements and interacts with them (75% success rate!)
• 🎯 Advanced Natural Interaction: Understands commands like "File", "Save button" with fuzzy matching
• 📍 Menu Intelligence: Detects menu bars, menu items, and UI hierarchies
• 🔍 Multi-Strategy Matching: Fuzzy text matching, position-based scoring, and semantic understanding
• ⚡ Proactive Help: Offers help before you need it
• 📊 Application Awareness: Monitors and broadcasts application events

🔧 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.

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