To ensure a healthy Model Context Protocol (MCP) service, focus on three categories of metrics: performance and reliability, resource efficiency, and application-specific quality. These metrics help identify bottlenecks, optimize resource usage, and validate that the service meets its intended goals. Below is a breakdown of key metrics to track in each category.
Performance and reliability metrics are critical to ensure the service responds quickly and consistently. Track latency (time taken per request) and throughput (requests processed per second) to gauge responsiveness under load. For example, if latency spikes above 500ms during peak traffic, it may indicate scaling issues. Monitor error rates (e.g., HTTP 5xx errors, timeouts) to catch failures early. Additionally, track uptime/availability (percentage of time the service is operational) and success rate (percentage of requests handled without errors). Tools like Prometheus or Grafana can visualize these metrics, and setting alerts for thresholds (e.g., 99% success rate) helps maintain reliability.
Resource efficiency metrics ensure the service uses infrastructure effectively. Measure CPU/memory usage to avoid over-provisioning or underutilization. For instance, sustained CPU usage above 80% may require scaling or code optimization. Track network bandwidth and disk I/O if the service handles large data transfers or frequent disk operations. For MCP services relying on caching, monitor cache hit rates—low rates suggest inefficient caching strategies. If the service uses GPUs/TPUs, track GPU utilization and memory consumption to optimize hardware costs. These metrics help balance performance with infrastructure costs.
Application-specific quality metrics validate the service’s functional goals. For MCP, this might include model accuracy (e.g., percentage of correct outputs) or context relevance scores (e.g., how well responses match user intent). Track data drift (changes in input data distribution) and model drift (decline in model performance over time) using statistical tests or monitoring tools like Evidently. If the service allows user feedback, collect user satisfaction scores (e.g., thumbs-up/down rates) to align outputs with expectations. For example, a drop in satisfaction scores could signal a need to retrain the model or adjust context-handling logic.
By combining these metrics, you can holistically monitor the health of an MCP service, address issues proactively, and ensure it delivers value efficiently.