How to implement error handling and retry mechanisms in MCP?

Implementing error handling and retry mechanisms in MCP is crucial for ensuring system stability and reliability. Here are detailed implementation strategies:

Error Handling Strategies

1. Error Classification

• Retryable Errors: Network timeouts, temporary service unavailability, rate limits, etc.
• Non-retryable Errors: Parameter errors, insufficient permissions, resource not found, etc.
• Business Errors: Business logic-related errors requiring special handling

2. Error Response Format

json
{
  "jsonrpc": "2.0",
  "id": "req-123",
  "error": {
    "code": -32000,
    "message": "Server error",
    "data": {
      "retryable": true,
      "retryAfter": 5,
      "details": "Database connection timeout"
    }
  }
}

3. Error Handling Implementation

python
from typing import Optional
import asyncio

class MCPErrorHandler:
    def __init__(self):
        self.retryable_codes = [
            -32000,  # Server error
            -32001,  # Timeout
            -32002   # Rate limit
        ]
    
    def is_retryable(self, error: dict) -> bool:
        """Determine if error is retryable"""
        error_code = error.get("code")
        return error_code in self.retryable_codes
    
    def get_retry_delay(self, error: dict) -> int:
        """Get retry delay time"""
        error_data = error.get("data", {})
        return error_data.get("retryAfter", 1)

Retry Mechanisms

4. Exponential Backoff Retry

python
import time
import random

async def exponential_backoff_retry(
    func,
    max_retries: int = 3,
    base_delay: float = 1.0,
    max_delay: float = 32.0
):
    """Exponential backoff retry mechanism"""
    last_exception = None
    
    for attempt in range(max_retries):
        try:
            return await func()
        except Exception as e:
            last_exception = e
            
            if attempt == max_retries - 1:
                raise
            
            # Calculate delay (with random jitter)
            delay = min(
                base_delay * (2 ** attempt) + random.uniform(0, 1),
                max_delay
            )
            
            await asyncio.sleep(delay)
    
    raise last_exception

5. Intelligent Retry Strategy

python
class RetryStrategy:
    def __init__(
        self,
        max_retries: int = 3,
        backoff_factor: float = 2.0,
        jitter: bool = True
    ):
        self.max_retries = max_retries
        self.backoff_factor = backoff_factor
        self.jitter = jitter
    
    async def execute_with_retry(
        self,
        func,
        is_retryable: Optional[callable] = None
    ):
        """Execute function with intelligent retry strategy"""
        for attempt in range(self.max_retries):
            try:
                return await func()
            except Exception as e:
                if attempt == self.max_retries - 1:
                    raise
                
                if is_retryable and not is_retryable(e):
                    raise
                
                delay = self._calculate_delay(attempt)
                await asyncio.sleep(delay)
    
    def _calculate_delay(self, attempt: int) -> float:
        """Calculate retry delay"""
        delay = self.backoff_factor ** attempt
        
        if self.jitter:
            delay += random.uniform(0, delay * 0.1)
        
        return delay

Circuit Breaker Pattern

6. Implementing Circuit Breaker

python
from enum import Enum
import time

class CircuitState(Enum):
    CLOSED = "closed"
    OPEN = "open"
    HALF_OPEN = "half_open"

class CircuitBreaker:
    def __init__(
        self,
        failure_threshold: int = 5,
        recovery_timeout: float = 60.0
    ):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.state = CircuitState.CLOSED
        self.failure_count = 0
        self.last_failure_time = None
    
    async def call(self, func):
        """Call function through circuit breaker"""
        if self.state == CircuitState.OPEN:
            if self._should_attempt_reset():
                self.state = CircuitState.HALF_OPEN
            else:
                raise Exception("Circuit breaker is OPEN")
        
        try:
            result = await func()
            self._on_success()
            return result
        except Exception as e:
            self._on_failure()
            raise
    
    def _should_attempt_reset(self) -> bool:
        """Determine if circuit breaker should attempt reset"""
        if self.last_failure_time is None:
            return False
        
        elapsed = time.time() - self.last_failure_time
        return elapsed >= self.recovery_timeout
    
    def _on_success(self):
        """Handle success"""
        self.failure_count = 0
        if self.state == CircuitState.HALF_OPEN:
            self.state = CircuitState.CLOSED
    
    def _on_failure(self):
        """Handle failure"""
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            self.state = CircuitState.OPEN

Monitoring and Logging

7. Error Monitoring

python
class ErrorMonitor:
    def __init__(self):
        self.error_counts = {}
        self.error_rates = {}
    
    def record_error(self, error_type: str):
        """Record error"""
        self.error_counts[error_type] = \
            self.error_counts.get(error_type, 0) + 1
    
    def get_error_rate(self, error_type: str) -> float:
        """Get error rate"""
        total = sum(self.error_counts.values())
        if total == 0:
            return 0.0
        
        return self.error_counts.get(error_type, 0) / total

Best Practices

1. Classify Error Types: Correctly identify retryable and non-retryable errors
2. Set Reasonable Retry Parameters: Adjust retry count and delay based on business scenarios
3. Implement Circuit Breaker: Prevent cascading failures
4. Detailed Logging: Record all errors and retry information
5. Monitoring and Alerting: Monitor error rates in real-time and set up alerts
6. Graceful Degradation: Provide fallback solutions when services are unavailable

Through these strategies, you can build a robust MCP system that effectively handles various error scenarios.