ScreenMonitorMCP
Enables real-time screen monitoring, UI element analysis, and predictive user behavior learning for AI assistants.
README
๐ Revolutionary Screen Monitor MCP Server
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!
๐ WHY ScreenMonitorMCP?
- ๐ฅ First & Only: Real-time continuous screen monitoring feature
- ๐ง AI Intelligence: AI that understands UI elements and can interact with them
- ๐ฎ Predictive: System that learns and predicts user behaviors
- โก Proactive: Assistant that offers help before you need it
- ๐ฏ Natural: AI that understands commands like "Click the save button"
๐ฅ REVOLUTIONARY FEATURES
๐ 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
๐ฏ 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
๐ง 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
๐ ๏ธ REVOLUTIONARY MCP TOOLS
๐ Real-Time Monitoring Tools
start_continuous_monitoring()- Starts AI's continuous vision capabilitystop_continuous_monitoring()- Stops continuous monitoringget_monitoring_status()- Real-time status information and statisticsget_recent_changes()- Recently detected screen changes
๐ฏ UI Intelligence Tools
analyze_ui_elements()- Recognizes and maps all UI elements on screensmart_click()- Smart clicking with natural language commands ("Click the save button")extract_text_from_screen()- OCR text extraction from screen
๐ง Predictive AI Tools
learn_user_patterns()- Learns and analyzes user behavior patternspredict_user_intent()- Predicts user intent based on current contextproactive_assistance()- Offers proactive help before user requestsrecord_user_action()- Records user actions and feeds learning system
๐ธ Traditional Tools
capture_and_analyze()- Screen capture and AI analysis (enhanced)list_tools()- MCP standard compliant lists all tools (categorized, detailed information)
๐ฏ USAGE SCENARIOS
๐ 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)
๐ฏ 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()
๐ง 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()
๐ 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
๐ง MCP CLIENT SETUP
Claude Desktop / MCP Client Configuration
Add the following JSON to your MCP client's configuration file:
๐ฏ Simple Configuration (Recommended)
{
"mcpServers": {
"screenMonitorMCP": {
"command": "python",
"args": ["/path/to/ScreenMonitorMCP/main.py"],
"cwd": "/path/to/ScreenMonitorMCP"
}
}
}
๐ง Advanced Configuration
{
"mcpServers": {
"screenMonitorMCP": {
"command": "python",
"args": [
"/path/to/ScreenMonitorMCP/main.py"
],
"cwd": "/path/to/ScreenMonitorMCP",
"env": {
"OPENAI_API_KEY": "your-api-key-here"
}
}
}
}
๐ก๏ธ Secure Configuration
{
"mcpServers": {
"screenMonitorMCP": {
"command": "python",
"args": [
"/path/to/ScreenMonitorMCP/main.py",
"--api-key", "your-secret-key"
],
"cwd": "/path/to/ScreenMonitorMCP"
}
}
}
๐ช Windows Example
{
"mcpServers": {
"screenMonitorMCP": {
"command": "python",
"args": ["C:/path/to/ScreenMonitorMCP/main.py"],
"cwd": "C:/path/to/ScreenMonitorMCP"
}
}
}
โ ๏ธ Important Notes
- File Path: Update
/path/to/ScreenMonitorMCP/main.pypath according to your project directory - Python Path: Make sure Python is in PATH or use full path:
"C:/Python311/python.exe" - Working Directory:
cwdparameter is important for proper.envfile reading - API Keys: All settings are automatically read from
.envfile
๐งช USAGE EXAMPLES
๐ 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()
๐ฏ 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}
)
๐ง 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"
)
๐ธ 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()
๐ REVOLUTIONARY CAPABILITIES
This MCP server gives AI the following capabilities:
- ๐๏ธ Continuous Vision: AI can monitor the screen non-stop
- ๐ง Smart Understanding: Recognizes UI elements and interacts with them
- ๐ฎ Future Prediction: Learns and predicts user behaviors
- โก Proactive Help: Offers help before you need it
- ๐ฏ Natural Interaction: Understands commands like "Click the save button"
๐ง TROUBLESHOOTING
Common Issues and Solutions
-
Unicode/Encoding Error (Windows)
UnicodeEncodeError: 'charmap' codec can't encode characterSolution: โ This error is fixed! Server automatically uses UTF-8 encoding.
-
JSON Configuration Error
// โ Wrong { "command": "python", "args": ["path/to/main.py",] // Trailing comma is wrong } // โ Correct { "command": "python", "args": ["path/to/main.py"] } -
Python Path Issue
{ "command": "C:/Python311/python.exe", // Use full path "args": ["C:/path/to/ScreenMonitorMCP/main.py"] } -
Missing Dependencies
cd ScreenMonitorMCP pip install -r requirements.txt -
OCR Issues
# Install Tesseract (optional) # EasyOCR installs automatically -
MCP Connection Closed Error
MCP error -32000: Connection closedSolution: Check file paths and add
cwdparameter.
๐ LICENSE
This project is licensed under the MIT License.
๐ Revolutionary MCP server that gives AI real "eyes"! ๐ฅ Next-generation AI-human interaction starts here!
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