Run a PromQL query against Control Plane metrics (Prometheus-compatible). Default is a range query over the last hour at 60s step — pass `resolution: "instant"` for a point-in-time query, `since` / `from` / `to` to adjust the window, and `step` to control resolution. Results are sliced to the first 50 series in prose; the full Prometheus response is included as JSON. If you already know the metric, just query it: gauges like `cpu_used`, `mem_used`, `replica_count` are used bare — as are the pre-rated `egress` and `requests_per_second` (never wrap these in rate()); genuine counters need rate(), e.g. `sum by (workload) (rate(container_restarts[5m]))`; latency is a histogram: `histogram_quantile(0.95, sum by (le) (request_duration_ms_bucket))`. Only when you are unsure of the exact metric name or label values — or a query returns no series — call `list_metrics` first to see what is actually present in the org (incl. custom metrics) and a metric’s real labels. Use this to verify autoscaling signals before changing scaling settings — measure first, then change.
ConnectorOAuth