Signoz Investigating Alerts

Alert Investigate

Diagnose why a SigNoz alert fired. The skill correlates the alert's own signal with neighbor signals around the fire window, and surfaces a ranked list of likely causes with supporting evidence. It is the companion to signoz-explaining-alerts: explain decodes the rule statically; investigate diagnoses a specific incident.

Prerequisites

This skill calls SigNoz MCP server tools heavily (signoz_get_alert, signoz_get_alert_history, signoz_execute_builder_query, signoz_query_metrics, signoz_search_traces, signoz_search_logs, signoz_get_trace_details, etc.). Before running the workflow, confirm the signoz_* tools are available. If they are not, the SigNoz MCP server is not installed or configured; run signoz-mcp-setup first to initialize or repair the MCP connection. The investigation depends on correlating multiple MCP queries; without the server there is no way to ground the analysis.

When to use

Use this skill when the user wants to:

• Understand why a specific alert fired.
• Find the root cause of a recent incident triggered by an alert.
• Correlate the alert's signal with related metrics, traces, and logs.
• Distinguish "real signal" fires from flapping or threshold-mistuning.

Do NOT use when the user wants to:

• Understand what an alert is configured to monitor → signoz-explaining-alerts.
• Create a new alert → signoz-creating-alerts.
• Modify an alert (raise threshold, add hysteresis) → call signoz_update_alert directly.
• Run a free-form ad-hoc investigation without an alert as the anchor → signoz-generating-queries.

Required inputs

If the alert name is fuzzy, this skill is best-effort (read-only):

1. Call signoz_list_alert_rules, paginate, fuzzy-match the name.
2. State the interpretation: "Investigating fire of 'High Error Rate - Checkout' (id 42) at 14:32 UTC. If you meant a different alert or fire, tell me."
3. Proceed.

If no firing transition exists in the queried lookback window, stop: there is nothing to investigate. Respond with:

"Alert '[name]' has not fired in the last 7d, so there is no fire window to investigate. Use signoz-explaining-alerts to walk through the rule, or check whether the alert is enabled."

Workflow

The investigation runs in three tiers with strict early-stop gates. Tier 1 always runs. Tier 2 runs only if tier 1 confirms a real fire. Tier 3 runs only if tier 2 surfaces correlated anomalies. Skipping the gates produces hundreds of unnecessary trace/log queries on quiet alerts.

Step 1: Resolve alert + fire window (Tier 0)

1. Resolve the alert id via signoz_list_alert_rules (paginated) if not given.
2. Call signoz_get_alert for the full rule config, needed to know what query, threshold, and resource scope the alert evaluated.
3. First call signoz_get_alert_history with timeRange: "7d" and order: "desc"; omit state so the timeline includes firing and inactive transitions. Continue only when data.nextCursor exists (the completeness note also reports hasMore: true). Pass it as cursor, replace timeRange with the note's resolved absolute start and end, and preserve the same state/filter (including omission) and order. Stop when nextCursor is absent / the note reports hasMore: false; never use offset or page fullness. If a later intentional state filter means "resolved" / "recovered", use inactive. The enum is inactive|pending|recovering|firing|nodata|disabled; recovering is a transient keep-firing state, not resolution. Pattern analysis needs the complete transition set. Rows are emitted per label-group fingerprint; do not interleave them. From the response:
   • Build rule-wide incident windows from distinct rows where overallStateChanged: true: an overallState: "firing" transition opens an incident; the next overallState: "inactive" closes it. Deduplicate matching timestamps and sort by unixMilli ascending before pairing. Default to the most recent incident unless $ARGUMENTS[1] selects another.
   • Partition affected series by fingerprint and retain each row's labels. Use only stateChanged: true rows to decide when that group fired and resolved, and which group should scope Tier 1–3 queries.
   • Note the fire pattern from rule-wide transitions or one named fingerprint:
     • one-off → single fire with a long quiet period before/after.
     • sustained → fires that stayed firing for ≥ 1 evaluation cycle.
     • flapping → ≥ 3 fires within a 1h window, alternating fire/resolve.
     • recurring → fires at regular intervals (cron-like, e.g., every hour).
   • Never infer flapping from different fingerprints. The pattern guides tiers 2/3.

Step 2: Tier 1 (what fired and how hard)

This tier always runs. It establishes the fire is real (vs. transient threshold tickle or flap) and quantifies the magnitude.

1. Re-run the alert's primary query over a window centered on the fire start: [fire_start - 30m, fire_start + 30m].
   • Use signoz_execute_builder_query for the alert's stored builder, formula, PromQL, or ClickHouse query envelope.
   • Preserve positive bounds/order so Tier 1 reproduces the stored alert. If a formula input is below 10000, record truncation risk and compare at 10000 before ruling groups out. For omissions, use 10000 on formula-input builder_query leaves and 100 on standalone/formula results. Find leaves from every formula expression, including disabled: true formulas, following references through the dependency graph. This walk sets comparison bounds only; it does not prove deterministic formula-to-formula order. Use v5 order: __result desc for metrics/formulas or primary aggregation desc for logs/traces, never dashboard orderBy. Time-series top-N ranks over the whole window and may omit a short-lived local spike.
2. Compute:
   • Peak value during the fire window.
   • Threshold breach magnitude: (peak - threshold) / threshold * 100 for "above" alerts, inverted for "below".
   • Fire duration: the rule's overall firing→inactive interval, or the selected fingerprint's interval for a group-scoped investigation. Say which.
   • Pre-fire baseline: average value in the 30m before fire start.
3. Early-stop gate: if the breach magnitude is < 10% over the threshold AND the fire duration is < 1 evaluation window, classify as "marginal fire"; the alert may be too sensitive. Skip tiers 2 and 3 and go to Step 5 with a single hypothesis: "threshold may be too tight, recommend tuning."

Step 3: Tier 2 (neighbor signals vs baseline)

Run only if Tier 1 confirms a real breach. Pull related signals for the same resource scope as the alert and compare the fire window to a baseline window.

1. Pick a baseline window. Use the same hour, previous day (fire_start - 24h, fire_start - 24h + fire_duration). If the alert fired during a known-anomalous time (deploy, weekly job), note it in the output but still proceed.

2. Look up neighbor signals for the alert's resource type. See references/neighbor-signals.md for the lookup table. Common cases:

   • Service-level alert (service.name = X): pull error rate, p95/p99 latency, request throughput, dependency error rates if trace data is available.
   • Host / VM alert (host.name = X): CPU, memory, disk I/O, network I/O.
   • K8s pod / namespace alert: pod restarts, container CPU/memory limits, node pressure, recent rollouts.

3. For each neighbor signal:

   • Query both windows (fire + baseline) via signoz_execute_builder_query or signoz_query_metrics.
   • Compute the delta (% change in fire window vs baseline).
   • Rank by absolute delta.

4. Early-stop gate: if no neighbor signal shows ≥ 25% deviation from baseline, classify as "isolated fire: the alert's own signal moved but nothing else did." This is unusual and worth surfacing. Skip Tier 3 and go to Step 5 with hypotheses focused on the alert's own query (likely causes: data source change, instrumentation change, downstream silent failure that only shows in this metric).

Step 4: Tier 3 (traces and logs at the fire window)

Run only if Tier 2 found correlated neighbor anomalies. Drill into specific failing operations.

1. Traces (if the alert is service-scoped and traces are available):

   • Call signoz_search_traces for the fire window with filter: service.name = <scope> AND has_error = true. Cap at top 20.
   • Group by name and status_message. Surface the sample's top 3 with one trace ID each; do not call a 20-row sample count full-window frequency.
   • Optionally call signoz_get_trace_details for span attributes. Pass the search row's trace_id as traceId plus the same absolute fire-window start and end; otherwise the 6h default misses older incidents.

2. Logs for the fire window:

   • Call signoz_search_logs with filter: <scope_filter> AND severity_text IN ('ERROR', 'FATAL'). Cap at top 20 most recent.
   • Group by body pattern (or exception.type if present). Surface the top 3 distinct messages with counts.

3. Cross-reference: do the traces and logs point at the same downstream service, dependency, or code path? If so, that becomes the leading hypothesis.

See references/baseline-comparison.md for query templates that pair fire-window and baseline-window calls cleanly.

Step 5: Build the structured output

Use this exact section order. Lead with a TL;DR, because engineers under pressure scan the top first and stop reading once they have what they need. Every claim cites the MCP query that produced it, with no generic "check logs / verify connectivity" filler.

1. TL;DR: one or two sentences, no more. Leading hypothesis, overall confidence, blast radius, and the single most useful next action. Example:

"checkoutservice error rate hit 12.4% (threshold 5%) for 8m at 14:32 UTC; most likely cause is payments-api timing out (high confidence). Open trace 7af3a09b… to see the failing call."

If no hypothesis reaches medium confidence, the leading line is "No clear root cause found." rather than a low-confidence guess dressed up as the answer.

2. What fired The alert (id, name), the fire window (absolute UTC + relative), peak magnitude ("error rate hit 12.4% vs. 5% threshold, 148% over"), fire duration, and the fire pattern (one-off / sustained / flapping / recurring / marginal).

3. Investigation trail A scannable list of what was checked, with ✅ for confirmed signals and ❌ for ruled out, each followed by a one-line finding. The point is that the reader can see what work the AI did and what it found. Example:

• ✅ Tier 1: peak error rate 12.4%, fire was real (not marginal).
• ✅ Tier 2: payments error rate +8900%, p99 +1180%; downstream cascade.
• ❌ CPU / memory pressure: flat through the fire window.
• ✅ Tier 3: 30 error traces all hit payments-api, same message.

4. Likely causes (ranked, max 3) Each cause has three parts:

• Hypothesis: one sentence, specific. Bad: "service is unhealthy". Good: "checkout is timing out on calls to payments-api".
• Evidence: the supporting numbers from tiers 1/2/3, with the underlying query inline so the user can re-run it. State the neighbor signal, the delta vs baseline, the trace/log pattern that supports it.
• Confidence: high requires ≥2 of: temporal precedence, topology / dependency edge, shared service or entity, correlated metric/log/trace evidence, recent deploy or config change. medium is one tier's evidence with at least one of those signals. low is a single signal moved with no corroboration; in that case label it a "co-occurring signal," not a cause.

If only Tier 1 ran (marginal fire / no neighbor anomalies), output fewer hypotheses with low confidence and explicitly call out the limitation.

5. Ruled out Short but explicit. List candidates the evidence eliminated and the one-line reason why. Skip the section if there's nothing meaningful to rule out, but if you considered something and dropped it, say so here so the user doesn't waste time re-checking it.

6. Suggested next steps Action items the user can take. Be concrete and use SigNoz-native handles so the user can act immediately:

• Specific trace, dashboard, or alert to open (e.g., "open trace 7af3a09b… in the SigNoz UI").
• Specific query to run with signoz-generating-queries: paste the exact filter and time window.
• "Tune this alert" if the fire was marginal: name the field (matchType, target, recoveryTarget) and the change to make via signoz_update_alert.
• "Open an incident" or "page the owning team" if the cause is cross-service.

Do not pad with generic advice ("verify connectivity", "check dashboards"); that's noise during an active incident.

Mirroring as navigation chips. Mirror up to 3 of these "Suggested next steps" as host follow-up intents: the most actionable, alert-scoped ones. Keep the rest in the report prose so the user has the full picture. The chip surface is capped; the prose is not.

Out of scope (v1)

• Deployment / config-change correlation: SigNoz MCP does not expose a deployments tool; do not fabricate one. If the user mentions a recent deploy, surface it as context but don't claim it caused the fire without the signal evidence.
• Cross-service blast-radius walking: investigating downstream callers of the alert's service. Out of scope to keep context bounded.
• Long-horizon historical baselines: Tier 2 compares to one prior-day window, not to weekly/monthly seasonality. If the user says "is this normal for a Friday afternoon", suggest an anomaly alert (signoz-creating-alerts with anomaly_rule).

Guardrails

• Three-tier early-stop is mandatory. Skipping the gates pulls hundreds of traces/logs on quiet alerts and explodes context. The gates are not optional optimizations.
• Anchor every claim to an MCP query result. No speculation. If evidence is missing, lower confidence and say so.
• Show the supporting query with each hypothesis so the user can reproduce and dig deeper.
• Keep it short and evidence-backed. TL;DR is one or two sentences max; the full report is a triage card, not a postmortem. Engineers under pressure should be able to skim the top and act. Every section earns its place by adding evidence the user couldn't already see in the alert payload.
• Correlation ≠ causation. Label something a cause only when at least two of the following converge: temporal precedence (signal moved before symptom), topology / dependency edge, shared service or entity, correlated metric/log/trace evidence, or a recent deploy/config change. A single time-aligned anomaly is a "co-occurring signal," not a cause; say so explicitly.
• Don't restate the alert or recommend the obvious. "Check logs", "verify connectivity", "investigate dashboards": the reader of this output already knows they need to. Replace generic suggestions with specific queries, traces, or filters they can run immediately.
• No fabricated identifiers. Trace IDs, span names, alert rule IDs, channel names, deploy IDs: every identifier in the output must come from a real MCP response. Don't invent placeholders that look plausible.
• Report uncertainty honestly. If no hypothesis reaches medium confidence, the answer is "No clear root cause found; here's what we checked and what's ruled out." Do not promote a low-confidence guess to the leading hypothesis just to sound useful. False positives waste active incident time more than false negatives.
• Prefer resource-attribute filters in every drill-down query. This is the SigNoz MCP guideline and it directly affects query speed at scale.
• Do not modify any alert. Investigate is read-only. If the user says "and tighten this alert", surface that as a next-step recommendation; do not call signoz_update_alert.
• Stay in scope. Static rule explanation belongs to signoz-explaining-alerts. Cause analysis without an alert anchor belongs to signoz-generating-queries.
• Time zones. Always state fire windows in UTC alongside relative time ("14:32 UTC, 2h ago") so autonomous and interactive consumers agree on the window.

Examples

User: "Why did the checkout error rate alert fire?"

Agent:

1. Resolves alert: "High Error Rate - Checkout" (id 42).

2. signoz_get_alert_history → most recent fire 2h ago at 14:32 UTC, sustained for 8m, single fire (not flapping).

3. Tier 1: re-runs error-rate formula over [14:02, 15:02]. Peak error rate 12.4% (vs 5% threshold, 148% over). Pre-fire baseline 0.3%. Real fire, not marginal.

4. Tier 2: pulls neighbor signals for service.name = checkout:

   • p99 latency: 4.1s vs 320ms baseline (+1180%).
   • Throughput: -42% (drop).
   • Downstream payments error rate: 18% vs 0.2% baseline (+8900%).
   • CPU/memory: flat (no resource pressure).

5. Tier 3: traces for service.name = checkout, has_error = true in the fire window: top operation POST /checkout/submit, top error message "context deadline exceeded calling payments-api". 30 traces, all hitting the same downstream URL. Logs show matching "payments client timeout" lines, 142 occurrences.

6. Output:

   TL;DR: checkoutservice error rate hit 12.4% (threshold 5%) for 8m at 14:32 UTC. Most likely cause: payments-api timing out (high confidence; converging trace + log + neighbor evidence). Open trace 7af3a09b… to see the failing call.

   • What fired: alert 42 fired 2h ago at 14:32 UTC, sustained 8m. Error rate peaked at 12.4% (148% over threshold).
   • Investigation trail:
     • ✅ Tier 1: peak 12.4% vs 5% threshold, pre-fire baseline 0.3%. Real fire.
     • ✅ Tier 2: payments error rate +8900%, p99 latency +1180%, throughput −42%.
     • ❌ CPU / memory pressure on checkout: flat.
     • ✅ Tier 3: 30 error traces all hit payments-api with context deadline exceeded; 142 matching timeout logs.
   • Likely causes (high confidence): payments service errors cascading into checkout. Evidence converges across topology (checkout → payments edge), temporal precedence (payments errors lead checkout p99), and shared entity (every error trace targets the payments-api URL).
   • Ruled out: checkout-side resource pressure (CPU/memory flat); upstream traffic spike (throughput dropped, didn't spike).
   • Next steps: open trace 7af3a09b… in the SigNoz UI; if payments has its own alert, run signoz-explaining-alerts on it, otherwise signoz-generating-queries for payments error rate over the last 4h.

User: "Investigate the flapping CPU alert on prod-api-3"

Agent:

1. Resolves alert (id 88, host.name = prod-api-3).

2. History: 7 fires in last 1h, alternating fire/resolve every 8-12 minutes: a flapping pattern.

3. Tier 1: peak 84% (threshold 80%, only 5% over). Each fire lasted 2-4 minutes. Marginal: the value hovered near threshold. matchType = at_least_once made each blip trigger.

4. Early-stop kicks in. Skip tiers 2/3.

5. Output:

   TL;DR: alert 88 has been flapping on prod-api-3: 7 fires in the last 1h, each 2–4m, all within 5% of the 80% threshold. No clear root cause; this is threshold tuning, not an incident. Switch matchType to on_average or add hysteresis to stop the noise.

   • What fired: alert 88, host prod-api-3, fire pattern flapping. 7 fires in the last 1h, each 2–4m. Peak 84% (5% over the 80% threshold).
   • Investigation trail:
     • ✅ Tier 1: every fire was within 5% of threshold; duration short; baseline already at 70–75%. Marginal fire; early-stop triggered, Tier 2/3 skipped.
   • Likely causes (low / co-occurring signal only): threshold tuned too tight or matchType is too sensitive. Evidence: every fire was within 5% of threshold; baseline already runs at 70–75%. Not promoted to a "cause": single signal, no corroboration.
   • Ruled out: real CPU saturation incident (peaks too small and short-lived; baseline already near threshold).
   • Next steps: change matchType to on_average (smooths transient spikes) OR raise threshold to 85% with hysteresis (recoveryTarget: 75). Use signoz_update_alert to apply.

User: "RCA for the log volume alert that fired last night"

Agent:

1. Resolves alert: "Error Log Volume Spike" (id 14, no service filter; groupBy service.name).

2. History: fired at 03:12 UTC, sustained 22m, broke down by service in the alert annotations: service.name = inventory was the firing series.

3. Tier 1: re-runs log count for inventory in fire window. Peak 3,400 errors/min vs 1,000/min threshold (240% over). Pre-fire baseline 12/min. Real, large fire.

4. Tier 2: neighbor signals for service.name = inventory:

   • Request error rate: +600%.
   • p99 latency: +30% (mild).
   • CPU: -80% (collapsed). Memory: -60%.
   • Pod restarts (k8s): 4 in fire window.

5. Tier 3: logs for inventory in fire window. Top message: "OOMKilled restarting" (1,200 occurrences). Top trace error: graceful-shutdown exceptions.

6. Output:

   TL;DR: log volume alert 14 fired at 03:12 UTC for service.name = inventory, sustained 22m at 240% over threshold. Most likely cause: inventory pods OOM-killed and restarted 4 times (high confidence). Check container memory limits for the inventory deployment.

   • What fired: alert 14 fired at 03:12 UTC for service inventory, sustained 22m, 240% over threshold.
   • Investigation trail:
     • ✅ Tier 1: peak 3,400 errors/min vs 1,000/min threshold; pre-fire baseline 12/min. Real fire.
     • ✅ Tier 2: request error rate +600%; CPU/memory collapsed (−80%/−60%); 4 pod restarts in window.
     • ❌ p99 latency: only +30%, not a latency-driven incident.
     • ✅ Tier 3: top log message "OOMKilled restarting" (1,200 occurrences); top trace error: graceful-shutdown exceptions.
   • Likely causes (high confidence): inventory pods OOM-killed and restarted 4 times during the window. Evidence converges across topology (single service), temporal precedence (memory fell to zero before error spike), shared entity (all log lines from service.name = inventory), and a single coherent pattern (OOM → restart → graceful-shutdown noise).
   • Ruled out: a true application error-rate change (errors are restart noise, not request-path failures); upstream traffic surge (throughput unchanged).
   • Next steps: check container memory limits for inventory pods; review recent deploys; consider whether the alert should exclude restart-related error patterns or whether the underlying OOM is the real concern.

Additional resources

• references/neighbor-signals.md: lookup table mapping resource type (service / host / k8s) to the neighbor signals to pull in Tier 2.
• references/baseline-comparison.md: query templates that pair fire-window and baseline-window calls cleanly, including how to format signoz_execute_builder_query for both.
• signoz-explaining-alerts skill: to decode the rule before investigating, if the user is unfamiliar with what the alert monitors.
• signoz-generating-queries skill: for ad-hoc follow-up queries on the same resource scope.