Marcus runs infrastructure for a 40-person SaaS company. Five engineers share on-call rotation. When a page fires at 2am, the first 10-15 minutes follow the same pattern every time: check PagerDuty for context, open Datadog to find the anomaly, cross-reference Grafana dashboards for historical patterns, check CloudWatch for the AWS-level view, then look at the internal status page to see what customers are seeing.
That workflow never changes. The information is always in the same places. Only the specific query differs.
During an average incident, Marcus switches between 5-7 browser tabs before forming a hypothesis. Most of that time is navigation, not thinking. He logs into Datadog, clicks through to the right dashboard, adjusts the time range, finds the metric. Repeat for every tool.
The worst part: junior engineers on the rotation spend even longer because they don't have the muscle memory for where to look first.
One Friday afternoon, Marcus created MCP servers for each tool in his monitoring stack:
| Tool | What it exposes | Time to set up |
|---|---|---|
| Datadog | Metrics queries, active monitors, recent events | ~90 seconds |
| PagerDuty | Current incidents, on-call schedule, alert history | ~60 seconds |
| Grafana | Dashboard panels, annotation queries, alert states | ~90 seconds |
| AWS CloudWatch | Log groups, metric data, alarm states | ~2 minutes |
| Internal status page | Component statuses, recent incidents, subscriber count | ~45 seconds |
Total setup time: under 8 minutes for all five.
When a page fires, Marcus (or whoever is on-call) opens Claude and asks:
Claude calls the relevant MCP servers, pulls live data, and presents a unified view. No tab switching. No remembering which dashboard shows which metric.
After two weeks of on-call rotations using the new setup:
Three things mattered:
1. All five tools have web UIs with API-backed frontends. DataFaucet captured the API calls that power each dashboard. No need for official API documentation (Grafana's internal API isn't even publicly documented well).
2. Claude correlates across tools automatically. Asking a question that spans Datadog and PagerDuty would require opening both and mentally joining the data. Claude does that join in the response.
3. On-call runbooks became prompts. The team's existing runbooks ("When you see X alert, check Y in Datadog and Z in Grafana") translated directly into natural language queries. New hires don't need to learn the runbook — they ask the question.
The monitoring stack use case works because these tools already have API-heavy frontends. If you can see it in a dashboard, DataFaucet can capture it.
Start with one tool — whichever you check most during incidents. Add more as you see the value compound from cross-tool correlation.
Free tier gets you 3 servers. Most teams start with Datadog + PagerDuty + one internal tool.
Create your Datadog MCP server in 60 seconds.
Try with Datadog →{
"mcpServers": {
"datadog": {
"url": "https://datafaucet.dev/api/mcp/YOUR_SERVER_ID/sse"
}
}
}Replace YOUR_SERVER_ID with the ID from your DataFaucet dashboard after creating your Datadog server.
Point DataFaucet at Datadog and get a working server in 60 seconds.
Create Datadog server free →After creating, add to Claude Desktop:
"datadog": {
"url": "https://datafaucet.dev/api/mcp/YOUR_ID/sse"
}Free plan includes 3 servers. Upgrade to Pro for unlimited →
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