# Task 4: Log Watcher Integration - Summary

## Overview
Successfully implemented real-time log watching capability for the MCP Debug Visualizer, connecting it to the structured logging output from the MCP debug server.

## Components Implemented

### 1. **log_watcher.py** - Robust File Monitoring
- **File polling** with configurable 0.1s interval
- **Partial line detection** - handles incomplete JSON lines gracefully
- **File rotation support** - detects inode changes and reopens file
- **Position persistence** - saves/loads position for resume capability
- **Thread-safe operation** - runs in separate thread to keep UI responsive
- **Error resilience** - handles missing files, permission errors, malformed JSON
- **Event injection** - support for testing with injected events

### 2. **log_parser.py** - Comprehensive Event Parsing
- Implements all event types from the logging specification:
  - `tool:call`, `tool:response`, `tool:error` - Tool lifecycle tracking
  - `session:created`, `session:closed` - Session management
  - `debug:state` - Execution state changes (paused/running/stopped)
  - `debug:breakpoint` - Breakpoint lifecycle (set/verified/hit)
  - `debug:variables` - Variable inspection with type info
  - `debug:output` - Console output from debugged program
- **Path normalization** using `Path.resolve()` for consistent display
- **Smart formatting** of tool details and responses
- **Duration formatting** for human-readable time displays
- **Value truncation** to prevent UI overflow

### 3. **live_visualizer.py** - Integration Layer
- Connects log watcher to the existing TUI visualizer
- Registers event handlers for all log types
- Provides clean startup/shutdown handling
- Accepts log file path as command-line argument
- Clear console output before entering TUI mode

### 4. **demo_runner.py** - Complete Demo Orchestration
- Starts MCP server with proper logging flags (`--log-level info --log-file`)
- Launches visualizer pointing to the log file
- **Robust process management**:
  - Graceful termination with timeout
  - Force kill as fallback
  - Cleanup handlers for Ctrl+C and SIGTERM
- Clear instructions for user interaction
- Pre-flight checks (server built, log directory exists)

### 5. **test_log_watcher.py** - Comprehensive Testing
- Tests all log watcher features:
  - Event detection and parsing
  - Partial line handling
  - Malformed JSON resilience
  - Position persistence
  - File rotation simulation
- Provides clear success/failure reporting

## Enhancements Beyond Requirements

1. **Partial Line Handling** - Detects and handles incomplete JSON lines by seeking back
2. **File Rotation Detection** - Uses inode tracking to detect log rotation
3. **Position Persistence** - Saves position to `.visualizer_position` for resume
4. **Timestamp Formatting** - Converts millisecond timestamps to readable HH:MM:SS.mmm
5. **Process Cleanup** - Robust cleanup with atexit and signal handlers
6. **Test Mode** - Event injection capability for testing without real logs

## Testing Results

### Log Watcher Test (`test_log_watcher.py`)
```
Test complete!
Events written: 6
Events received: 6
Success rate: 6/6
✅ All tests passed
```

### Mock Demo Test
- Visualizer continues to work correctly with mock data
- All UI components render properly
- State management functions as expected

## Usage

### Option A: Manual Setup
```bash
# Terminal 1: Start MCP server
node dist/index.js --log-level info --log-file logs/debug-mcp-server.log

# Terminal 2: Start visualizer
cd examples/visualizer
python live_visualizer.py ../../logs/debug-mcp-server.log

# Terminal 3: Use MCP client to debug
```

### Option B: Demo Runner (Recommended)
```bash
cd examples/visualizer
python demo_runner.py
```

## Architecture Benefits

1. **Decoupled Design** - Log watcher runs independently in its own thread
2. **Event-Driven** - Clean callback registration system
3. **Resilient** - Handles various failure modes gracefully
4. **Testable** - Can be tested independently of MCP server
5. **Extensible** - Easy to add new event types

## Edge Cases Handled

1. **Missing log file** - Creates file if it doesn't exist
2. **Partial JSON lines** - Detects and waits for completion
3. **Malformed JSON** - Silently skips invalid lines
4. **File rotation** - Detects and reopens new file
5. **Permission errors** - Graceful degradation with error messages
6. **Process cleanup** - Ensures no zombie processes

## Performance Considerations

1. **Efficient polling** - 0.1s interval balances responsiveness and CPU usage
2. **Thread safety** - No blocking of UI thread
3. **Memory management** - Tool activity limited to 20 items
4. **File caching** - Code window caches loaded files

## Success Criteria ✅

- [x] Log watcher successfully tails the JSON log file
- [x] All event types from the specification are handled
- [x] TUI updates in real-time as events occur
- [x] File paths are properly normalized and displayed
- [x] Breakpoints and current line tracking work from logs
- [x] Variables are parsed and displayed correctly
- [x] Demo runner successfully orchestrates both components
- [x] Graceful handling of errors and edge cases

## Next Steps

With Task 4 complete, the visualizer is now ready for:
- **Task 5**: Demo recording and GIF creation
- **Task 6**: Screenshots capture
- **Task 7**: README integration

The live log watching capability transforms the visualizer from a mock demo tool into a real debugging companion that can visualize actual MCP debug sessions in real-time.
