Observe Metrics

Aggregate counters, gauges, and histograms from the observability namespace and flag anomalies.

When to use

When you need a snapshot of system health -- task completion rates, error rates, active agent counts, memory usage, and token consumption. Useful for monitoring swarm performance and detecting degradation.

Steps

1. Retrieve metrics -- call mcp__claude-flow__memory_search --namespace observability (or memory_list) to fetch metric records for the specified period (default: 1 hour). The memory_* tool family routes by namespace; agentdb_hierarchical-* does NOT, so use memory_* here.
2. Aggregate -- compute:
   • Counters: sum totals (tasks_completed, errors, token_usage)
   • Gauges: current values (active_agents, memory_usage_bytes)
   • Histograms: p50, p95, p99 (task_duration_ms, span_duration_ms)
3. Compute baselines -- call mcp__claude-flow__agentdb_pattern-search (ReasoningBank-routed; don't pass a namespace argument — pattern-* tools ignore it) to establish baseline values for each metric.
4. Flag anomalies -- mark metrics deviating >2 standard deviations from baseline with direction (above/below) and severity
5. Store patterns -- two paths (per ruflo-cost-tracker ADR-0001 dual-path pattern):
   • Pattern store (typed, recommended): mcp__claude-flow__agentdb_pattern-store with type: 'metric-snapshot'. No namespace arg.
   • Plain store (namespace-routable): mcp__claude-flow__memory_store --namespace observability for the snapshot tied to a timestamp.
6. Report -- display: metric name, current value, baseline, deviation, trend (up/down/stable), anomaly flag; overall health score (green/yellow/red)

CLI alternative

npx @claude-flow/cli@latest memory search --query "system metrics for last hour" --namespace observability