Three months ago, one of my MCP servers crashed at 2 AM on a Friday. I didn't find out until Monday morning when a customer opened a support ticket. By then, 60+ API calls had failed silently, and I'd lost two days of data.
That's when I realized: MCP servers have no built-in observability. They fail quietly. There's no error dashboard, no alerts, no uptime tracking.
I spent 8 weeks building a monitoring stack for MCP servers. Here's what I learned.
The Problem
Unlike traditional SaaS APIs, MCP servers often:
- Run on VPS with minimal logging
- Don't have native error tracking
- Fail gracefully but return garbage responses
- Have no built-in health check endpoints
- Don't expose metrics in standardized format
What to Monitor
After my 2 AM incident, I identified five critical metrics:
- Uptime & Availability — Is the server actually handling requests?
- Error Rates — What percentage fail? I set 2% threshold.
- Response Times — p50, p95, p99 latency
- Token Usage — MCP servers burn tokens fast
- Resource Utilization — Memory, CPU, disk
Real Lessons Learned
Lesson 1: Server crashes aren't your biggest problem.
The real problem was the server with 500ms latency for 3 days. Customers quietly switched competitors.
Lesson 2: Logs are not metrics.
You can't alert on logs. Use structured metrics instead.
Lesson 3: Test your health checks.
My health check always returned green, even when the core service failed.
Lesson 4: Set realistic thresholds.
My 1% error threshold got me paged 40 times/day. Now 5%.
Conclusion
Monitoring isn't sexy. But it's the difference between customers discovering outages and you discovering them first.
Cost: 2 hours setup + $50-100/month infrastructure. Upside: Not woken at 2 AM.
What's your monitoring approach?
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