MCP-Logic

# ErrorHandling ## Purpose and Overview The error handling system in the MCP Logic server is designed to provide robust, informative, and recoverable error management. Key aspects include: 1. Error Categories: - Syntax errors in logical formulas - Proof execution errors - Resource management issues - Integration failures - System-level errors 2. Error Management Goals: - Clear error identification - Detailed error context - Recovery mechanisms - Debugging support - Performance impact tracking 3. Error Handling Strategy: - Hierarchical error classification - Contextual error information - Graceful degradation - Automatic recovery attempts - Comprehensive logging [Why this domain is critical to the project] ## Step-by-Step Explanations ### Error Hierarchy Implementation 1. Base Error Classes: ```python class LogicError(Exception): """Base class for all MCP Logic errors""" def __init__( self, message: str, details: Optional[dict] = None ): super().__init__(message) self.details = details or {} self.timestamp = datetime.now() class ProverError(LogicError): """Errors from the Prover9/Mace4 system""" pass class SyntaxError(LogicError): """Formula syntax errors""" pass class ResourceError(LogicError): """Resource management errors""" pass class IntegrationError(LogicError): """External system integration errors""" pass ``` 2. Error Context Management: ```python class ErrorContext: def __init__(self): self.error_stack = [] self.context_data = {} def add_context(self, key: str, value: Any): self.context_data[key] = value def push_error(self, error: Exception): self.error_stack.append({ 'error': error, 'context': self.context_data.copy(), 'timestamp': datetime.now() }) def get_error_history(self) -> List[dict]: return self.error_stack ``` 3. Error Recovery Implementation: ```python class ErrorRecovery: def __init__(self, max_retries: int = 3): self.max_retries = max_retries self.current_retries = 0 async def with_recovery( self, operation: Callable, *args, **kwargs ) -> Any: while self.current_retries < self.max_retries: try: return await operation(*args, **kwargs) except Exception as e: self.current_retries += 1 if self.current_retries >= self.max_retries: raise await self.handle_error(e) async def handle_error(self, error: Exception): if isinstance(error, ResourceError): await self.cleanup_resources() elif isinstance(error, IntegrationError): await self.reconnect() await asyncio.sleep(self.current_retries) ``` ### Error Logging Implementation 1. Structured Logging: ```python class LogicLogger: def __init__(self): self.logger = logging.getLogger('mcp_logic') self.setup_logging() def setup_logging(self): formatter = logging.Formatter( '%(asctime)s - %(name)s - %(levelname)s - %(message)s' ) handler = RotatingFileHandler( 'mcp_logic.log', maxBytes=10000000, backupCount=5 ) handler.setFormatter(formatter) self.logger.addHandler(handler) def log_error( self, error: LogicError, level: int = logging.ERROR ): self.logger.log(level, str(error), extra={ 'error_type': type(error).__name__, 'details': error.details, 'timestamp': error.timestamp }) ``` ### Error Monitoring Implementation 1. Error Metrics Collection: ```python class ErrorMetrics: def __init__(self): self.error_counts = defaultdict(int) self.error_durations = defaultdict(list) def record_error( self, error: LogicError, duration: float ): error_type = type(error).__name__ self.error_counts[error_type] += 1 self.error_durations[error_type].append(duration) def get_error_stats(self) -> dict: stats = {} for error_type in self.error_counts: stats[error_type] = { 'count': self.error_counts[error_type], 'avg_duration': ( sum(self.error_durations[error_type]) / len(self.error_durations[error_type]) ), 'max_duration': max(self.error_durations[error_type]) } return stats ``` [Concrete, detailed steps for implementation and maintenance] ## Annotated Examples [Code snippets, diagrams, or flowcharts for clarity] ## Contextual Notes [Historical decisions, trade-offs, and anticipated challenges] ## Actionable Advice [Gotchas, edge cases, and common pitfalls to avoid]