Business Problem
On-call engineers face alert fatigue and spend the first 15-30 minutes of every incident gathering context, checking dashboards, and running diagnostic commands before they can even begin fixing the issue.
Solution Overview
Connect PagerDuty, Datadog, and Kubernetes MCP Servers to create automated runbooks that gather context, run diagnostics, attempt auto-remediation, and prepare a summary for the on-call engineer.
Implementation Steps
Configure Monitoring Integration
Connect the Datadog MCP Server to receive alerts and query metrics programmatically.
Set Up Kubernetes Access
Configure the Kubernetes MCP Server with cluster credentials for diagnostic commands.
Build Runbook Templates
Create runbook templates for common incidents: high CPU, OOM kills, pod crash loops, high error rates.
Implement Auto-Diagnosis
When an alert fires, automatically gather logs, metrics, recent deployments, and pod status.
async function diagnoseIncident(alert) {
const [metrics, pods, deploys, logs] = await Promise.all([
datadog.queryMetrics({ query: alert.metric, from: '-15m' }),
kubernetes.listPods({ namespace: alert.namespace, labelSelector: alert.service }),
kubernetes.listDeployments({ namespace: alert.namespace }),
kubernetes.getPodLogs({ name: alert.pod, tail: 100 })
]);
return { metrics, pods, deploys, logs };
}Auto-Remediate Known Issues
For known patterns like crash loops, automatically restart pods or roll back deployments.
Code Examples
async function gatherContext(alert) {
const context = await diagnoseIncident(alert);
const summary = `## Incident Summary\n- **Alert**: ${alert.name}\n- **Pods affected**: ${context.pods.length}\n- **Recent deploys**: ${context.deploys.length}\n- **Error pattern**: ${extractPattern(context.logs)}`;
await slack.sendMessage({ channel: '#incidents', text: summary });
}