The Challenge
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.
The Approach
Solution Overview
Connect Elasticsearch MCP Server with Slack and PagerDuty to continuously analyze logs for anomalous patterns and intelligently alert based on severity.
Step-by-Step
Implementation Steps
1
Stream Log Data
Configure Elasticsearch MCP Server to query recent log entries across all services.
2
Detect Anomalies
Compare log patterns against historical baselines: error rates, response times, unusual messages.
3
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` });
}
}4
Generate Daily Digest
Summarize daily log health including error trends, slow endpoints, and resolved anomalies.
Code
Code Examples
typescript
Anomaly Detector
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 };
}Overview
ComplexityMedium
Estimated Time~12 hours
Tools Used
Elasticsearch MCP ServerSlack MCP ServerPagerDuty MCP Server
Industry
TechnologySaaSFinance
ROI Metrics
Time Saved10 hours/week
Cost Reduction60% fewer production incidents
Efficiency Gain15-minute earlier detection