macOS Simulator MCP Server

# Mac Commander - Comprehensive Usage Examples This guide showcases all the powerful features and improvements in Mac Commander through practical, real-world scenarios. From basic automation to advanced workflows, you'll learn how to leverage the full capabilities of this MCP server. ## Table of Contents 1. [Getting Started](#getting-started) 2. [Performance Optimization Examples](#performance-optimization-examples) 3. [Advanced UI Detection](#advanced-ui-detection) 4. [OCR with Fuzzy Matching](#ocr-with-fuzzy-matching) 5. [Complex Automation Workflows](#complex-automation-workflows) 6. [Testing and Debugging](#testing-and-debugging) 7. [Real-World Application Scenarios](#real-world-application-scenarios) 8. [Performance Monitoring](#performance-monitoring) 9. [Best Practices](#best-practices) ## Getting Started ### Basic Screenshot and Analysis ``` "Take a screenshot and save it to my desktop as 'app-state.png'" ``` **What happens**: Uses optimized screenshot capture with automatic memory management and saves to specified location. ``` "Capture just the top toolbar region from coordinates (0,0) to (1200,100) and return compressed base64 data" ``` **What happens**: Region-specific capture with 60-80% compression for efficient data transfer. ## Performance Optimization Examples ### Before/After Performance Comparison #### Before (Memory Issues) ``` User: "Extract text from the entire screen 10 times in a row" ``` **Old behavior**: Memory usage climbs to 99%, system becomes unresponsive, potential crashes. #### After (Optimized Performance) ``` User: "Extract text from the entire screen 10 times in a row" ``` **New behavior**: - Memory usage stays at 60-70% through intelligent buffering - 60-80% faster text operations via optimized OCR processing - Smart caching provides 30-70% hit rates for repeated operations - Automatic garbage collection prevents memory exhaustion ### Smart Caching in Action ``` "Take a screenshot, then extract text from it, then find 'Submit' button, then take another screenshot of the same area" ``` **Performance benefits**: - First screenshot: Full capture and processing - Text extraction: Cached OCR results from previous screenshot - Text search: Uses cached text data, no re-processing needed - Second screenshot: Cache hit if screen hasn't changed (30-70% faster) ### Memory Management Example ``` "Process this large application window: take screenshot, extract all text, find 5 different UI elements, then take verification screenshots" ``` **Automatic optimizations**: - Chunked image processing for large screenshots - Progressive memory cleanup during long operations - Throttling prevents system overload - Built-in garbage collection triggers maintain stability ## Advanced UI Detection ### Smart Button Detection (Beyond Text) #### Modern Apps with Icon-Only Buttons ``` "Find all clickable buttons in this interface, even if they don't have text labels" ``` **Response**: ```json { "totalElementsFound": 8, "elements": [ { "type": "button", "description": "Circular button with blue background (#007AFF), likely primary action", "position": { "x": 450, "y": 200, "width": 44, "height": 44, "center": { "x": 472, "y": 222 } }, "clickable": true, "confidence": 0.95, "detectionMethods": ["color_analysis", "shape_detection", "size_validation"], "visualFeatures": { "backgroundColor": "#007AFF", "borderRadius": 22, "hasShadow": true } }, { "type": "button", "text": "Save", "description": "Text button with system accent color", "position": { "x": 520, "y": 300, "width": 60, "height": 32, "center": { "x": 550, "y": 316 } }, "clickable": true, "confidence": 0.98, "detectionMethods": ["text_recognition", "color_analysis", "context_analysis"] } ] } ``` ### Complex Form Detection ``` "Analyze this registration form and identify all input fields, buttons, and interactive elements" ``` **Advanced capabilities**: - Detects text fields without visible borders - Identifies dropdown menus by arrow indicators - Recognizes checkboxes and radio buttons by shape - Groups related form elements by proximity - Validates minimum touch target sizes (44x44 pixels) ### macOS-Specific UI Recognition ``` "Find all native macOS interface elements in this system dialog" ``` **Specialized detection**: - Recognizes standard macOS button styles - Identifies system color schemes (light/dark mode) - Detects Apple Human Interface Guidelines compliance - Understands native dialog patterns and layouts ## OCR with Fuzzy Matching ### Handling OCR Variations #### Basic Text Search with Fuzzy Matching ``` "Find 'Submit' button on screen" ``` **Fuzzy matching handles**: - "Subm1t" (OCR misread '1' for 'i') - "SUBMIT" (case variations) - "submit" (lowercase) - "Subrrit" (character confusion) - "Sub mit" (spacing issues) #### Advanced Fuzzy Search Example ``` "Look for any buttons containing 'Continue' or similar text" ``` **Matches found**: ```json { "found": true, "matches": [ { "text": "Continue", "confidence": 0.98, "similarity": 1.0, "center": { "x": 400, "y": 500 } }, { "text": "Cont1nue", "confidence": 0.85, "similarity": 0.87, "center": { "x": 600, "y": 300 } }, { "text": "CONTINUE", "confidence": 0.92, "similarity": 0.95, "center": { "x": 200, "y": 400 } } ], "bestMatch": { "text": "Continue", "center": { "x": 400, "y": 500 } } } ``` ### OCR Configuration for Different Scenarios #### High-Accuracy Mode ``` "Configure OCR for maximum accuracy and find the exact text 'Product ID: ABC-123'" ``` **Configuration applied**: - Minimum confidence: 80% - Fuzzy threshold: 90% - Relaxed fallback: 70% #### Fast Processing Mode ``` "Quick scan for any text containing 'error' or 'warning' - prioritize speed over accuracy" ``` **Configuration applied**: - Minimum confidence: 30% - Fuzzy threshold: 60% - Relaxed fallback: 40% - Extended cache TTL for faster responses ## Complex Automation Workflows ### Complete Application Testing Workflow ``` "Help me test my invoice application end-to-end: 1. Take a screenshot of the current state 2. Find and click the 'New Invoice' button 3. Wait for the form to load (look for 'Customer Name' field) 4. Fill in customer details using realistic typing 5. Add line items using tab navigation 6. Find the total amount and verify it's calculated correctly 7. Click Save and wait for success confirmation 8. Take a final screenshot to document the result" ``` **Advanced features demonstrated**: - Performance monitoring throughout the workflow - Automatic waiting for dynamic content - Human-like typing with variable delays - Error detection and handling - Visual verification at each step - Memory-efficient operation across long workflows ### File Management Automation ``` "Organize my desktop files: 1. Take a screenshot to see current state 2. Select all image files using visual detection 3. Create a new folder called 'Images' 4. Drag all image files to the new folder 5. Verify the operation completed successfully" ``` **Demonstrates**: - Advanced drag-and-drop with smooth animation - Visual file type recognition - Folder creation through system interactions - Verification of completed operations ### Cross-Application Workflow ``` "Copy data from Safari to Numbers: 1. Focus the Safari window 2. Find and select the data table using visual detection 3. Copy the selection (cmd+c) 4. Focus Numbers application 5. Create a new spreadsheet if needed 6. Paste the data and format it appropriately 7. Save the Numbers document" ``` **Advanced capabilities**: - Window management and focus switching - Visual table detection and selection - Cross-application data transfer - Automatic application state management ## Testing and Debugging ### Automated UI Testing with Visual Validation ``` "Test the login flow with visual verification: 1. Take baseline screenshot 2. Click login button and verify modal appears 3. Enter test credentials with realistic typing 4. Click submit and monitor for error messages 5. Verify successful login or capture error states 6. Document all visual states for comparison" ``` **Testing features**: - Baseline comparison capabilities - Error state detection and capture - Performance metrics for each step - Comprehensive visual documentation ### Error Detection and Recovery ``` "Monitor this process for errors and automatically retry if needed: 1. Click the submit button 2. Wait 3 seconds and check for error dialogs 3. If errors found, capture details and try alternative approach 4. If successful, continue with next steps" ``` **Error handling capabilities**: - Automatic error dialog detection - Smart retry mechanisms with exponential backoff - Error categorization and reporting - Recovery strategy implementation ### Performance Regression Testing ``` "Benchmark this UI operation for performance: 1. Measure time to take screenshot 2. Time the OCR text extraction 3. Monitor memory usage during operation 4. Compare against baseline performance metrics 5. Report any significant performance changes" ``` **Performance monitoring**: - Real-time performance tracking - Memory usage monitoring - Baseline comparison - Regression detection and alerting ## Real-World Application Scenarios ### E-commerce Testing ``` "Test the complete checkout process: 1. Navigate to product page 2. Select product options using visual UI detection 3. Add to cart and verify cart icon updates 4. Proceed to checkout 5. Fill shipping information with realistic delays 6. Select payment method 7. Review order details and submit 8. Capture confirmation screen" ``` **E-commerce specific features**: - Product option selection (dropdowns, color swatches) - Cart state monitoring - Form validation handling - Payment flow testing - Order confirmation verification ### Content Management System Automation ``` "Create and publish a blog post: 1. Access CMS admin interface 2. Click 'New Post' button using visual detection 3. Enter post title and content with natural typing 4. Upload and position images using drag-and-drop 5. Set categories and tags using form controls 6. Preview the post and check formatting 7. Publish and verify front-end display" ``` **CMS automation features**: - Rich text editor interaction - File upload handling - Category/tag management - Preview mode testing - Publication workflow verification ### Data Entry and Migration ``` "Transfer customer data from spreadsheet to CRM: 1. Open source spreadsheet and select data range 2. Copy data with proper formatting preservation 3. Switch to CRM application 4. Navigate to import function 5. Paste data and map fields correctly 6. Validate data accuracy after import 7. Generate import summary report" ``` **Data migration capabilities**: - Bulk data selection and copying - Application switching and navigation - Field mapping and validation - Data accuracy verification - Import reporting and documentation ### Accessibility Testing ``` "Test keyboard navigation and screen reader compatibility: 1. Navigate entire interface using only keyboard 2. Verify all interactive elements are reachable 3. Test tab order and focus indicators 4. Check for proper ARIA labels and roles 5. Verify color contrast and visual indicators 6. Document accessibility compliance issues" ``` **Accessibility features**: - Keyboard-only navigation testing - Focus indicator verification - Color contrast analysis - ARIA attribute checking - Compliance reporting ## Performance Monitoring ### Real-Time Performance Dashboard ``` "Show me the current performance status of the Mac Commander system" ``` **Dashboard Response**: ```json { "systemHealth": { "overall": "healthy", "components": { "tools": "healthy", "resources": "healthy", "cache": "healthy", "queues": "healthy", "ocr": "healthy" }, "uptime": 3600000 }, "resourceMetrics": { "cpuUsagePercent": 15.2, "memoryUsagePercent": 65.8, "loadAverage": [1.2, 1.1, 0.9] }, "cacheMetrics": [ { "name": "screenshot", "hitRate": 0.72, "size": 45 }, { "name": "ocr", "hitRate": 0.58, "size": 23 } ], "recommendations": [ "Cache hit rate is good (72% for screenshots)", "Memory usage is optimal at 65.8%", "Consider increasing OCR cache TTL for better hit rates" ] } ``` ### Performance Trends Analysis ``` "Analyze the performance trend for screenshot operations over the last hour" ``` **Trend Analysis**: ```json { "timestamps": [1640995200000, 1640995800000, 1640996400000], "values": [350, 320, 280], "trend": "improving", "anomalies": [], "analysis": "Screenshot operation time has improved 20% over the last hour, likely due to cache warming and memory optimization" } ``` ### Performance Optimization Recommendations ``` "Give me specific recommendations to improve system performance" ``` **Optimization Suggestions**: - Enable aggressive garbage collection for memory-intensive operations - Increase screenshot cache size for frequently accessed images - Implement request batching for multiple OCR operations - Consider pre-warming caches for common UI elements - Optimize region-based screenshots for better performance ## Best Practices ### Efficient Workflow Design #### ✅ Recommended Approach ``` "Take a screenshot first, then extract text, then search for specific elements - reuse the same screenshot data" ``` **Benefits**: - Single screenshot capture with multiple analyses - Leverages caching for better performance - Reduces memory usage and processing time #### ❌ Inefficient Approach ``` "Extract text from screen, take screenshot, extract text again, take another screenshot" ``` **Problems**: - Multiple unnecessary captures - Cache misses and repeated processing - Higher memory usage and slower execution ### Memory Management Best Practices #### Large Operations ``` "For processing large screenshots or long automation sequences: 1. Use region-based captures when possible 2. Enable progressive cleanup during long operations 3. Monitor memory usage and trigger GC when needed 4. Break complex workflows into smaller batches" ``` #### Cache Optimization ``` "Optimize caching strategy: 1. Use longer TTL for stable UI elements 2. Shorter TTL for dynamic content 3. Clear caches between major workflow changes 4. Monitor cache hit rates and adjust accordingly" ``` ### Error Handling Strategies #### Robust Automation ``` "Build resilient automation: 1. Always check for error dialogs after critical actions 2. Implement retry logic with exponential backoff 3. Use visual verification for important state changes 4. Capture screenshots for debugging failed operations" ``` #### Graceful Degradation ``` "Handle edge cases gracefully: 1. Fall back to approximate matching if exact text isn't found 2. Use alternative UI detection methods if primary fails 3. Provide meaningful error messages with context 4. Maintain operation logs for troubleshooting" ``` ### Testing and Quality Assurance #### Comprehensive Test Coverage ``` "Design thorough testing workflows: 1. Test happy path scenarios 2. Verify error conditions and edge cases 3. Validate performance under load 4. Check accessibility compliance 5. Document all test scenarios and results" ``` #### Continuous Monitoring ``` "Implement ongoing quality checks: 1. Monitor performance metrics continuously 2. Set up alerts for threshold violations 3. Track success rates and error patterns 4. Regular performance regression testing" ``` ## Advanced Configuration Examples ### OCR Optimization for Different Scenarios #### High-Accuracy Document Processing ```javascript // Configure for maximum accuracy configureOCR({ minConfidence: 85, fuzzyMatchThreshold: 0.95, relaxedFuzzyThreshold: 0.8, cacheEnabled: true, cacheTTL: 60000 // Longer cache for documents }); ``` #### Fast UI Element Detection ```javascript // Configure for speed over accuracy configureOCR({ minConfidence: 40, fuzzyMatchThreshold: 0.7, relaxedFuzzyThreshold: 0.5, cacheEnabled: true, cacheTTL: 5000 // Shorter cache for dynamic UI }); ``` #### Memory-Constrained Environment ```javascript // Configure for minimal memory usage configureOCR({ minConfidence: 60, cacheEnabled: false, // Disable cache to save memory maxCacheSize: 25, timeoutMs: 15000 // Shorter timeout }); ``` ### Performance Tuning Examples #### High-Throughput Processing ``` "Configure the system for processing many screenshots rapidly: 1. Enable request batching for parallel operations 2. Increase cache sizes for better hit rates 3. Use region-based captures to reduce processing time 4. Enable performance monitoring for optimization" ``` #### Resource-Conscious Operation ``` "Optimize for systems with limited resources: 1. Enable automatic throttling and memory management 2. Use smaller batch sizes to prevent overload 3. Implement progressive cleanup during operations 4. Monitor resource usage and adjust accordingly" ``` ## Troubleshooting Common Scenarios ### Performance Issues #### High Memory Usage ``` "System is using too much memory during automation" ``` **Solutions**: 1. Check cache sizes and reduce if necessary 2. Enable automatic garbage collection 3. Use region-based screenshots instead of full screen 4. Break large operations into smaller chunks 5. Monitor memory usage with performance dashboard #### Slow OCR Operations ``` "Text extraction is taking too long" ``` **Solutions**: 1. Reduce OCR confidence thresholds for faster processing 2. Use smaller regions for text extraction 3. Enable OCR result caching 4. Consider using UI element detection for non-text elements 5. Optimize fuzzy matching parameters ### Automation Reliability #### Intermittent Click Failures ``` "Clicks sometimes don't register properly" ``` **Solutions**: 1. Add small delays before and after clicks 2. Use visual verification after critical clicks 3. Implement retry logic with error detection 4. Check for UI state changes that might block interactions 5. Use hover before click for better targeting #### Text Not Found Issues ``` "Text search fails even when text is visible" ``` **Solutions**: 1. Enable fuzzy matching with lower thresholds 2. Try different OCR confidence settings 3. Use region-based search for better accuracy 4. Check for font or rendering issues affecting OCR 5. Consider using UI element detection as fallback ## Future-Proofing Your Automation ### Adaptive Strategies #### Dynamic UI Handling ``` "Design automation that adapts to UI changes: 1. Use multiple detection strategies (text + visual) 2. Implement fallback mechanisms for element detection 3. Monitor for UI pattern changes over time 4. Build flexible coordinate systems based on relative positioning" ``` #### Cross-Platform Considerations ``` "Prepare for different macOS versions and configurations: 1. Test across different screen resolutions 2. Verify behavior in light and dark modes 3. Account for different system font sizes 4. Handle accessibility features like increased contrast" ``` ### Scalability Planning #### Enterprise Deployment ``` "Scale Mac Commander for enterprise use: 1. Implement centralized performance monitoring 2. Set up automated health checks and alerting 3. Design modular workflows for easy maintenance 4. Plan for load balancing across multiple systems" ``` #### Integration Architecture ``` "Integrate with existing automation frameworks: 1. Design standardized APIs for external systems 2. Implement webhook notifications for workflow events 3. Plan for data export and analysis capabilities 4. Consider security and access control requirements" ``` --- ## Summary Mac Commander provides a comprehensive automation platform with advanced features for UI detection, OCR processing, performance optimization, and complex workflow automation. The examples in this guide demonstrate how to leverage these capabilities for real-world scenarios while following best practices for reliability, performance, and maintainability. Key takeaways: - **Performance**: 60-80% faster operations with intelligent caching and memory management - **Reliability**: Advanced error detection and recovery mechanisms - **Flexibility**: Multiple detection strategies for robust UI interaction - **Scalability**: Built-in monitoring and optimization for enterprise use - **Maintainability**: Comprehensive logging and debugging capabilities Whether you're automating simple tasks or building complex testing frameworks, Mac Commander provides the tools and performance you need for successful macOS automation.