Trace Debugging

Use this skill when you have a trace_id and need to understand the end-to-end behavior of a single request.

When to dispatch the agent instead

If trace_request returns >200 logs OR the trace spans >5 services, stop and dispatch the graylog-trace-analyzer subagent. It returns a synthesized timeline (~50 lines) instead of flooding the main context with raw logs. Inline investigation is for smaller, focused traces.

Playbook

Step 1 — Pull the trace

Call trace_request with:

• traceId: the trace ID the user gave you (or you extracted from a Sentry/dashboard URL).
• from / to: a generous window — ±15 minutes around the suspected event. Trace IDs are unique enough that overshooting is cheap.
• limit: 200 to start. Raise to 500 if you see truncation hints.

The response groups logs by service and orders by timestamp. Read it once before doing anything else.

Step 2 — Identify the failure span

Scan the trace for:

• The first error or fatal level entry — that is the proximate failure.
• The originating service — usually the entry-point (gateway, API server) or the service that emitted the first error.
• Latency outliers — gaps > 1s between consecutive spans often indicate a hung dependency.

Step 3 — Pull surrounding context for each error span

For each error span, call get_surrounding_logs with:

• centerTimestamp: the error timestamp.
• source or pod: filter to the same pod, since adjacent context from other pods is noise.
• secondsBefore / secondsAfter: start with 5 each.

The error message itself is rarely the cause. The cause is usually 1–10 lines before, in the same pod, often at info or warn level (e.g., a connection timeout, a config reload, a retry exhaustion).

Step 4 — Build the timeline

Assemble a human-readable summary:

• Originating service / pod
• Trace duration (first timestamp to last)
• Error spans in chronological order, each with: timestamp, service, message, likely cause from surrounding context
• Propagation: did the error originate downstream and bubble up, or originate at the entry point?

Pitfalls

• Window too narrow. Long-running jobs (batch processors, background workers) can have trace IDs alive for hours. If trace_request returns very few results, widen the window before assuming the trace is small.
• Missing trace_id on some lines. Not every log emits trace_id. The trace_request results only show lines tagged with the ID. Use get_surrounding_logs to pull adjacent untagged context.
• Re-used trace IDs. Rare but possible if a service is misconfigured to reuse IDs. If you see two clearly-different requests under one ID, widen from/to and check the timestamps.
• Clock skew. Pods on different hosts can have skewed clocks. A "downstream first" timeline order can be an artifact of skew, not actual causation.

What to return to the user

A 5–10 line synthesis:

• Originating service / pod
• Root-cause line (from surrounding context, not the propagated error)
• Blast radius (how many downstream services were affected)
• Suggested next step (look at deploy times? check dependency health? read a specific service's recent commits?)