MCP Servers Are Infrastructure
Once your team relies on MCP servers for daily work, they're infrastructure. When the GitHub MCP server goes down, your AI assistant can't create PRs. When the database server is slow, every query takes forever. When a server silently starts returning errors, your agent workflows break in confusing ways. You need to know when these things happen.
Most MCP servers don't come with built-in monitoring. They're designed to be lightweight protocol bridges, not full-featured services. That means monitoring is something you'll add yourself, and the approach depends on how you're running your servers.
Health Checks and Heartbeats
The simplest monitoring is a periodic health check. Every few minutes, send a lightweight tool call to each server and verify you get a valid response. For a filesystem server, list the contents of a known directory. For a database server, run a simple query. For a Git server, list repositories. If the response comes back correctly, the server is healthy. If it doesn't, alert.
You can implement this with a cron job, a monitoring service like Uptime Kuma, or even a simple script. The key metrics to track are: response time (is the server getting slower?), success rate (are calls failing?), and availability (is the server up at all?). Log these to a time-series database so you can spot trends.
Error Rate Tracking
Beyond binary up/down monitoring, track the error rate of actual tool calls. A server might be "up" in the sense that it responds to health checks, but return errors for 30% of real tool calls because of an upstream API rate limit or authentication issue.
If your MCP servers log to stdout (most do), you can parse those logs for error patterns. Pipe them through something like Promtail into Loki, or ship them to whatever log aggregation you use. Set up alerts for error rate spikes: "alert if more than 10% of tool calls in the last 5 minutes returned errors." This catches degradation that health checks miss.
Check the MCP ecosystem stats page to understand baseline reliability expectations for popular servers.
Latency Monitoring
Slow MCP servers make AI assistants feel sluggish. If a tool call that normally takes 200ms starts taking 3 seconds, your users notice even if the call eventually succeeds. Track p50, p95, and p99 latency for each server and each tool. Alert on significant deviations from baseline.
Latency spikes often indicate upstream issues: the API the server connects to is slow, the database is under load, or the server itself is running out of memory. Having latency data helps you diagnose these issues quickly instead of guessing.
Practical Alert Configuration
Start with these alerts and adjust based on your experience: server unreachable for more than 2 minutes, error rate above 10% for 5 minutes, p95 latency more than 3x the baseline for 10 minutes, and authentication failures (which usually mean expired credentials). Route alerts to wherever your team already gets notified: Slack, PagerDuty, email.
Don't over-alert. If you're getting woken up at 3am because a non-critical MCP server had a brief latency spike, you'll start ignoring alerts entirely. Tier your servers by criticality and set alert severity accordingly. Production deployment pipeline servers get page-level alerts. Personal productivity servers get informational notifications.