# Phase 4: Advanced Features & Monitoring

## Overview  
Implement advanced monitoring, configuration management, and operational features that make the MCP server production-ready with comprehensive observability and management capabilities.

## Prerequisites
- Phase 1-3 completed: Full MCP server with Flask subprocess management
- Basic health monitoring working
- MCP protocol handlers operational

## Requirements

### 1. Advanced Health Monitoring

#### Multi-Level Health Checks
Implement comprehensive health monitoring across multiple layers:

- **MCP Server Health**:
  - Protocol handler responsiveness
  - Memory and CPU usage monitoring
  - Active connection counts
  - Request/response latencies

- **Flask App Health**:  
  - HTTP endpoint responsiveness (`/health`)
  - Database/file system access times
  - Memory usage and performance metrics
  - Active request counts

- **System Health**:
  - Disk space availability
  - File system permissions
  - Network connectivity
  - Resource constraints

#### Health Dashboard
Create a comprehensive health status system:
```python
class HealthMonitor:
    def get_health_status(self):
        return {
            "overall_status": "healthy|degraded|unhealthy",
            "mcp_server": {
                "status": "healthy",
                "uptime": 3600,
                "requests_handled": 1500,
                "avg_response_time": 0.05
            },
            "flask_app": {
                "status": "healthy", 
                "url": "http://127.0.0.1:5000",
                "last_health_check": "2024-01-01T12:00:00Z",
                "response_time": 0.02
            },
            "system": {
                "disk_usage": "45%",
                "memory_usage": "2.1GB",
                "prompts_count": 42,
                "last_backup": "2024-01-01T06:00:00Z"
            }
        }
```

#### Performance Metrics
- **Response time tracking**: Measure and log MCP request/response times
- **Throughput monitoring**: Track requests per second/minute  
- **Resource usage**: CPU, memory, and disk I/O monitoring
- **Error rate tracking**: Monitor and alert on error patterns

### 2. Configuration & Environment Detection

#### Environment Detection System
Automatically detect and adapt to different runtime environments:

- **MCP Mode Detection**:
  - Check if running under MCP client (environment variables)
  - Detect parent process information
  - Configure logging and behavior accordingly

- **Standalone Mode Support**:
  - Command-line interface for standalone MCP server
  - Configuration file support for non-MCP usage
  - Integration with existing Flask standalone mode

- **Development vs Production**:
  - Automatic debug mode detection
  - Logging level adjustment
  - Performance optimization toggles

#### Configuration Management System
```python
class ConfigManager:
    def __init__(self):
        self.config = self.load_config()
        
    def load_config(self):
        # Priority order: env vars > config file > defaults
        # Support for .env files, JSON, YAML
        # Validation and type coercion
        
    def get_flask_config(self):
        # Flask-specific configuration
        
    def get_mcp_config(self): 
        # MCP server configuration
        
    def is_production_mode(self):
        # Determine if running in production
        
    def get_log_config(self):
        # Logging configuration
```

#### Status Endpoints
Add endpoints to Flask app for operational visibility:

- **`/mcp-status`**: MCP integration status and configuration
- **`/config`**: Current configuration (sanitized, no secrets)
- **`/metrics`**: Performance and usage metrics
- **`/debug-info`**: Development debugging information (dev mode only)

### 3. Advanced Process Management

#### Process Monitoring & Analytics
- **Process genealogy**: Track parent-child process relationships
- **Resource usage tracking**: Detailed CPU, memory, and I/O metrics
- **Process lifecycle events**: Startup, restart, shutdown event logging
- **Performance profiling**: Optional profiling for performance optimization

#### Intelligent Restart Logic
```python
class RestartManager:
    def __init__(self, config):
        self.failure_history = []
        self.backoff_strategy = ExponentialBackoff()
        
    def should_restart(self, failure_reason):
        # Analyze failure patterns
        # Apply backoff strategy
        # Consider failure thresholds
        
    def execute_restart(self, restart_reason):
        # Log restart event with context
        # Execute graceful restart
        # Update monitoring metrics
```

#### Resource Management
- **Memory limits**: Monitor and enforce memory usage limits
- **CPU throttling**: Prevent resource exhaustion
- **Disk space monitoring**: Alert on low disk space
- **Connection limits**: Prevent resource exhaustion from connections

### 4. Operational Features

#### Logging & Observability
- **Structured logging**: JSON-formatted logs with correlation IDs
- **Log aggregation**: Centralized logging for both MCP server and Flask app
- **Audit trail**: Track all configuration changes and administrative actions
- **Performance logging**: Detailed performance metrics and timing data

#### Administrative Interface
Extend MCP protocol with administrative capabilities:

- **Server statistics**: Real-time server performance and usage statistics
- **Configuration updates**: Dynamic configuration updates without restart
- **Process control**: Graceful restart, shutdown commands via MCP
- **Health reporting**: Detailed health reports for monitoring systems

#### Backup & Recovery
- **Configuration backup**: Automatic backup of configuration changes
- **Data integrity**: Verify prompt data integrity on startup
- **Recovery procedures**: Documented recovery from common failure scenarios
- **State persistence**: Preserve important runtime state across restarts

### 5. Integration & Compatibility

#### MCP Client Compatibility
- **Protocol versioning**: Support multiple MCP protocol versions
- **Client identification**: Identify and adapt to different MCP clients
- **Feature negotiation**: Advertise and negotiate supported features
- **Backwards compatibility**: Maintain compatibility with older clients

#### External Integration
- **Webhook support**: Optional webhooks for important events
- **Metrics export**: Export metrics in standard formats (Prometheus, etc.)
- **Monitoring integration**: Integration with standard monitoring tools
- **Log shipping**: Support for external log aggregation systems

### 6. Error Handling & Diagnostics

#### Advanced Error Handling
- **Error classification**: Categorize errors by severity and type
- **Error recovery**: Automatic recovery for transient errors
- **Error reporting**: Structured error reporting with context
- **Circuit breaker**: Prevent cascade failures

#### Diagnostic Tools
```python
class DiagnosticManager:
    def run_diagnostics(self):
        # Comprehensive system diagnostics
        # Configuration validation
        # Connectivity tests
        # Performance benchmarks
        
    def generate_debug_report(self):
        # Complete debug information
        # System state snapshot
        # Recent error history
        # Performance metrics
```

## Deliverables
1. Advanced health monitoring system with multi-level checks
2. Comprehensive configuration management with environment detection
3. Enhanced process monitoring and intelligent restart logic
4. Operational status endpoints and administrative interface
5. Structured logging and observability features
6. Diagnostic tools and error handling improvements
7. Integration capabilities for external monitoring systems

## Testing Requirements
- Test health monitoring under various failure conditions
- Verify configuration loading from different sources
- Test process monitoring and restart logic
- Validate status endpoints and administrative features
- Test logging and observability features
- Verify diagnostic tools and error handling
- Test integration with external monitoring systems

## Success Criteria
- Comprehensive visibility into system health and performance
- Reliable operation under various runtime environments
- Effective error handling and recovery mechanisms
- Clear operational status and diagnostic capabilities
- Production-ready monitoring and observability
- Seamless integration with external tools and systems
- Maintainable and debuggable system architecture

## Configuration Schema
```yaml
# promptbin-config.yaml
mcp:
  protocol_version: "1.0"
  max_connections: 100
  request_timeout: 30
  
flask:
  port: 5000
  workers: 4
  max_memory: "512MB"
  
monitoring:
  health_check_interval: 30
  metrics_retention: "24h" 
  enable_profiling: false
  
logging:
  level: "INFO"
  format: "json"
  rotation: "daily"
  
operations:
  backup_interval: "6h"
  max_restart_attempts: 5
  graceful_shutdown_timeout: 30
```

## Notes
- Focus on production readiness and operational excellence
- Implement observability best practices
- Design for easy troubleshooting and maintenance
- Consider scalability and performance implications
- Document all operational procedures thoroughly
- Test extensively under failure conditions
- Plan for future extensibility and feature additions