Business Problem
Teams rely on static alerting rules that either fire too often (alert fatigue) or miss novel failure modes. Log analysis is reactive rather than proactive.
Solution Overview
Connect Elasticsearch MCP Server with Slack and PagerDuty to continuously analyze logs for anomalous patterns and intelligently alert based on severity.
Implementation Steps
Stream Log Data
Configure Elasticsearch MCP Server to query recent log entries across all services.
Detect Anomalies
Compare log patterns against historical baselines: error rates, response times, unusual messages.
Classify and Alert
Route alerts based on severity and type to appropriate channels.
async function analyzeLogWindow() {
const logs = await elasticsearch.search({ index: 'app-logs-*', query: { range: { '@timestamp': { gte: 'now-5m' } } } });
const errorRate = logs.filter(l => l.level === 'error').length / logs.length;
const p99Latency = percentile(logs.map(l => l.duration), 99);
if (errorRate > errorBaseline * 3) {
await pagerduty.createIncident({ title: `Error rate spike: ${(errorRate*100).toFixed(1)}%`, severity: 'high' });
} else if (p99Latency > latencyBaseline * 2) {
await slack.sendMessage({ channel: '#alerts', text: `⚠️ Latency degradation: p99 = ${p99Latency}ms` });
}
}Generate Daily Digest
Summarize daily log health including error trends, slow endpoints, and resolved anomalies.
Code Examples
function detectAnomalies(current, baseline, stdDev) {
const zScore = (current - baseline) / stdDev;
if (zScore > 3) return { level: 'critical', zScore };
if (zScore > 2) return { level: 'warning', zScore };
return { level: 'normal', zScore };
}