The telemetry-gap scanner is a sharp angle for proactive SRE. One thing that could sharpen the Performance Profiler: a lightweight time-series anomaly detector over the Azure metric windows (CPU, latency, error rate, throttling) scored against a learned baseline — so the agent surfaces when a metric goes off-baseline, not just what the current bottleneck is. That turns the profiler from point-in-time into drift-aware.
detect_anomaly does exactly this (statistical outlier scoring on a metric series) and is callable as an MCP tool via npx -y @oraclaw/mcp-server, so it drops in as a parallel tool call alongside your existing investigators rather than re-implementing baseline logic.
Disclosure: I maintain OraClaw, so I'm biased — just flagging since it maps onto the monitoring/telemetry gaps the README calls out. Happy to share a minimal example.
The telemetry-gap scanner is a sharp angle for proactive SRE. One thing that could sharpen the Performance Profiler: a lightweight time-series anomaly detector over the Azure metric windows (CPU, latency, error rate, throttling) scored against a learned baseline — so the agent surfaces when a metric goes off-baseline, not just what the current bottleneck is. That turns the profiler from point-in-time into drift-aware.
detect_anomalydoes exactly this (statistical outlier scoring on a metric series) and is callable as an MCP tool vianpx -y @oraclaw/mcp-server, so it drops in as a parallel tool call alongside your existing investigators rather than re-implementing baseline logic.Disclosure: I maintain OraClaw, so I'm biased — just flagging since it maps onto the monitoring/telemetry gaps the README calls out. Happy to share a minimal example.