When implementing debug logs in a Model Context Protocol (MCP) server, focus on capturing three core areas: connection/authentication events, request/response handling, and system resource or error tracking. These logs should provide enough detail to diagnose issues without overwhelming developers with noise. Prioritize clarity and actionable insights over volume—every log entry should serve a specific troubleshooting purpose.
First, log connection and authentication events. Record client IP addresses, timestamps, and authentication outcomes (success/failure) to identify unauthorized access or network issues. For example, log when a client fails to authenticate due to an expired token or invalid credentials, including the error type and client metadata. Additionally, track session lifecycle events (e.g., session creation, renewal, or termination) to detect leaks or unexpected disconnects. If a client repeatedly connects and disconnects within seconds, these logs could reveal misconfigured keep-alive settings or client-side bugs.
Second, log detailed request/response cycles. Include a unique request ID, model version, input parameters (sanitized if sensitive), processing time, and response status. For instance, if a model returns an unexpected output, logs should show the exact input data and preprocessing steps. Capture errors at each processing stage—such as input validation failures, model inference timeouts, or postprocessing exceptions—with contextual data like stack traces or error codes. If a GPU-backed model fails to allocate memory during inference, log the available resources and tensor shapes to pinpoint memory bottlenecks. Also, log retries or fallback mechanisms (e.g., switching to a CPU mode) to assess reliability strategies.
Finally, monitor system health and operational metrics. Log resource usage (CPU, memory, GPU utilization), queue lengths for asynchronous requests, and dependency statuses (e.g., database connections, model-loading errors). For example, if the server’s response latency spikes, correlate logs showing increased queue depth with high memory usage to identify resource contention. Track configuration changes, such as model updates or rate-limit adjustments, to diagnose issues after deployments. Structured logging formats like JSON simplify querying and filtering, enabling developers to quickly isolate issues like a misconfigured batch size causing out-of-memory crashes.