Does Prometheus replace Grafana?

No. Prometheus collects and stores the time-series data; Grafana (or Prometheus's own expression browser) is typically used to visualize it.

Should the /metrics endpoint require authentication?

Yes if it's reachable outside your own network - it can reveal server names, tool names, and call volume. Put it behind the same auth boundary as the rest of your infrastructure, not the public MCP endpoint.

How often should Prometheus scrape?

15-30 seconds is a common default. Faster scraping increases storage and CPU cost without meaningfully improving alerting latency for most MCP workloads.

monitoringHowTo

Monitor MCP Servers with Prometheus

Export MCP server metrics in Prometheus format and write PromQL queries for tool latency, error rate, and throughput.

Quick Answer / TL;DR

Prometheus monitors MCP servers by scraping a /metrics endpoint that exposes counters and histograms for tool calls, then lets you query and alert on them with PromQL.

Key Takeaways

Expose a /metrics endpoint with the prom-client library rather than pushing metrics manually.
Use a histogram for tool-call duration so you can derive p50/p95/p99, not just an average.
Label by server and tool name, never by user ID, email, or request payload content.

Instrumenting an MCP server

The prom-client package is the standard way to expose Prometheus-format metrics from a Node.js process. Wrap each tool handler so every call increments a counter and records its duration in a histogram, then serve the aggregated result on a /metrics HTTP endpoint for Prometheus to scrape on an interval.

typescript

import { Counter, Histogram, register } from "prom-client";
import express from "express";

const toolCalls = new Counter({
  name: "mcp_tool_calls_total",
  help: "Total tool calls handled",
  labelNames: ["server", "tool", "status"],
});

const toolDuration = new Histogram({
  name: "mcp_tool_duration_seconds",
  help: "Tool call duration in seconds",
  labelNames: ["server", "tool"],
  buckets: [0.05, 0.1, 0.25, 0.5, 1, 2, 5],
});

async function callTool(server: string, tool: string, handler: () => Promise<unknown>) {
  const end = toolDuration.startTimer({ server, tool });
  try {
    const result = await handler();
    toolCalls.inc({ server, tool, status: "success" });
    return result;
  } catch (err) {
    toolCalls.inc({ server, tool, status: "error" });
    throw err;
  } finally {
    end();
  }
}

const app = express();
app.get("/metrics", async (_req, res) => {
  res.set("Content-Type", register.contentType);
  res.end(await register.metrics());
});
app.listen(9464);

Useful PromQL queries

Once Prometheus is scraping the endpoint, these queries cover the metrics that matter most for an MCP server: error rate, p95 latency, and per-tool call volume.

Question	PromQL
Error rate over 5 minutes	sum(rate(mcp_tool_calls_total{status="error"}[5m])) / sum(rate(mcp_tool_calls_total[5m]))
p95 tool latency	histogram_quantile(0.95, sum(rate(mcp_tool_duration_seconds_bucket[5m])) by (le, tool))
Calls per tool per minute	sum(rate(mcp_tool_calls_total[1m])) by (tool)