Business Problem
Teams use 5-10 different monitoring tools. When an alert fires, engineers switch between dashboards gathering context. There's no single pane of glass for system health.
Solution Overview
Connect Prometheus, Grafana, and Datadog MCP Servers with Slack to create a unified monitoring agent that correlates signals across tools and runs remediation playbooks.
Implementation Steps
Aggregate Metrics
Pull key metrics from Prometheus, Datadog, and application health endpoints into a unified model.
Correlate Signals
When one metric degrades, automatically check related metrics across other tools for correlation.
Execute Playbooks
For known patterns, automatically run remediation steps.
async function handleAlert(alert) {
const context = await gatherCrossToolContext(alert);
const playbook = matchPlaybook(alert, context);
if (playbook) {
await executePlaybook(playbook, context);
await slack.sendMessage({ channel: '#ops', text: `Auto-remediated: ${alert.name} using playbook '${playbook.name}'` });
} else {
await slack.sendMessage({ channel: '#ops', text: `Manual investigation needed: ${alert.name}\n\nContext:\n${formatContext(context)}` });
}
}Track Resolution Metrics
Log MTTR, auto-remediation success rate, and alert-to-resolution times.
Code Examples
async function gatherCrossToolContext(alert) {
const [promMetrics, ddMetrics, appHealth] = await Promise.all([
prometheus.query({ query: `rate(http_errors_total{service='${alert.service}'}[5m])` }),
datadog.queryMetrics({ query: `avg:system.cpu.user{service:${alert.service}}`, from: '-15m' }),
fetch(`${alert.service}/health`).then(r => r.json())
]);
return { promMetrics, ddMetrics, appHealth, alert };
}