Investigate

You are finding out why something broke. Most investigations stall in one of three places: the window or service is wrong, a filter silently matched nothing, or the agent read raw rows before it knew which slice of traffic was bad. The sequence below avoids all three. Follow it in order, then go deeper with the companion files.

Prerequisites

This skill requires the remotely hosted LaunchDarkly MCP server. Tools it uses:

• Scope: find-o11y-project, get-keys, get-incidents
• Shape and comparison: search-aggregations (with groupBy, compareTo, exact, series and formulas), search-service-map (with include: ["health"])
• What changed: get-changes
• Evidence: get-trace, search-traces, search-logs, search-log-patterns, search-error-groups, get-error-group, search-sessions, get-session-details, search-timeline-events
• Alerts: get-alerts, get-alert-firing-history, get-investigation (see alert-investigation)

If a tool is missing, the MCP server may not be connected or may be older; say so rather than working around it. Some response fields named below (low_sample, ingest, self_ms, summary on firing history, alert_episode, error-group include) arrived in a recent server release. If a response lacks one, the server predates it: use the manual fallback given next to it, and do not report the field as empty.

The first five minutes

Do these six steps before reading pages of raw rows. Each one is one to three calls. Keep a short running note of the window, the filter and what each step ruled in or out.

1. Scope and onset

Pin down the project, the service and the time the problem started.

• Project. If the user did not name one, call find-o11y-project with the service name. Ask if there is no single clear match.
• Service family. One system often reports under several names (production-api, staging-api, production-api-worker). Call get-keys with key: "service_name" and search: "<name>" and pick the exact values. Filter with services: [...] or service_name=(a OR b), not service_name=*api*: a * is a contains match and pulls in siblings.
• Incident. If the report names an incident (INC-123), call get-incidents with incidentId. Its created_at is when someone noticed, not when it began; start your window at least 2 hours earlier.
• Onset. Run search-aggregations on the failing signal (error logs, 5xx spans, error groups) over a window wider than the report, for example 6 to 24 hours, with bucketCount 24 to 48. Find the first bucket that departs from the line. That is your anomaly window; the same-length window just before it, or the same time a day or week earlier, is your baseline.
• Data completeness. Before you conclude that data is missing or that a count fell, check what ingest drops. get-keys adds an ingest block when a non-default setting applies to the product (sampling rate, rate limit, exclusion query, filter rules, quota), and search-aggregations adds it with includeIngest: true. Its completeness_note, when present, names in one line what is reducing the data. A drop in errors right after a quota was exceeded is not a recovery.
• Shape. One spike, a step up, a slow drift, or the same pattern every day? A daily pattern points to traffic or batch jobs, not a regression.

Use ISO 8601 timestamps with a timezone from here on (2026-10-29T13:30:00Z). compareTo needs them; relative dates such as now-1h are rejected with it.

2. Is it chronic, or noise?

Before hunting for a cause, check that something actually changed.

• If an alert is involved, call get-alert-firing-history with lookbackDays: 7. Its summary says chronic: true (with chronic_reason) when the alert fired on more than 4 of the last 7 days, and lists the episodes; on a server without summary, dozens of firings across the week on the same group mean the same thing. Chronic means a standing condition or a fragile threshold, not a new incident. See alert-investigation.
• Run the failing signal again with compareTo: "1w" (or "1d") and bucketCount: 1. If the anomaly window’s value is close to previous_values, the problem was already there last week: say so, and investigate it as a standing issue, not a regression.

3. What changed?

Call get-changes with serviceName (one exact service_name value), startDate 1 to 2 hours before the onset, and endDate just after it. Set environmentKey (for example production) and keep the window short: it merges build versions, flag changes and alert episodes, and a long window on a busy project gets truncated (the newest rows are kept; truncation_note says how to get the rest).

• A version whose first_seen is just before the onset is the first suspect. Note the versionKey it detected (service_version, tags.version or version); use that same key in step 4. Services differ, so do not guess it.
• A flag change with evaluated_by_service: true is the strongest flag link: the service’s own spans evaluated that flag in the window. By default only those are returned (or, when the service recorded no evaluations at all, the changes in environmentKey); sources.flags.omitted counts what was left out. Set allFlagChanges: true only when you need every flag change in the project. On an older server every change is returned and you must filter on evaluated_by_service yourself.
• The audit log is sampled across the window up to maxFlagChanges; sources.flags.sampling and slices_with_more say whether some slices had more. When covered_from is present, only the newest page was read: if it is after your onset, narrow the window rather than trusting the gap.
• Alert episodes: alert_episode rows merge firings less than 2 hours apart, with firings, last_fired_at and group_by_key. An episode that started before your onset is background, not the trigger.
• No change near the onset is a finding too: look at traffic, a dependency, or one bad host next.

A change that lines up in time is a suspect, not a conclusion. Step 4 tests it.

4. What is over-represented now?

This is the step that finds the cause most often, and the one agents skip. Compare the share of each value of a dimension in the anomaly window against the baseline. A cause shows up as one value that holds most of the failures now but only its usual share of traffic.

1. List candidate dimensions with get-keys for the product type (search: "host", search: "version", search: "route", search: "region", search: "pod"). Confirm a key exists before you group or filter on it: an unknown key in a filter is dropped silently and widens the result.

2. Break the failures down by each candidate, one search-aggregations call per dimension:

   jsonCopy

   {
     "productType": "traces",
     "startDate": "<anomaly start>", "endDate": "<anomaly end>",
     "query": "span_kind=Server has_errors=true",
     "services": ["<service>"],
     "groupBy": "host.name", "groupByLimit": 10, "bucketCount": 1,
     "compareTo": "1d"
   }

   For 5xx only, filter on the status-code key instead; services differ (http.response.status_code, http.status_code), so check which one the service sets with get-keys search=status_code and a filtered count before relying on it. Then run the same call without the error filter (all requests) so you can compare each value’s share of failures with its share of traffic.

3. Dimensions to try, in this order: host.name (or k8s.pod.name), the version key from step 3, http.route (with span_kind=Server), environment or region, and for flag-gated code the flag variation (below). For latency, break down P95 of duration the same way and set rankBy to {"aggregator":"Count","column":""} so busy groups rank first.

4. Error rate per value in one call. For traces, drop the error filter and use expressions [{"aggregator":"Count","column":""},{"aggregator":"Sum","column":"has_errors"}] with rankBy Count: each row then has its requests and its failed spans side by side. For any other ratio, use series and formulas: series a all requests, series b failures, formula 100 * b / a. Each series ranks its own top groups, so a value outside one series’ top groups returns a null formula; raise groupByLimit or set minCount if rows come back null.

5. Read it as shares. “Host X served 9 % of requests and 94 % of 5xx” is a cause. “Version Y has the most errors” is not, if it also has the most traffic. When one host is bad and a new version is on many hosts, group by [versionKey, "host.name"] to see whether the version also runs on healthy hosts. If it does, the version is not the cause.

6. Flag variations. The FLAG FILTERS keys feature_flag.key, feature_flag.result.value, feature_flag.result.reason.kind and feature_flag.context.key work in the traces, logs and errors queries: on errors and logs they keep records from requests that evaluated that value in the same trace, on traces the span’s own evaluation events. Break failures down with query: "feature_flag.key=<flag>" and groupBy: "feature_flag.result.value", with and without the error filter, and compare shares as above. For the frontend view, search-flag-evaluations without sessionSecureId counts a flag’s evaluations project-wide: pass flagKey, optionally environmentKey, and variation with include: ["error_groups", "sessions"] to list the error groups sessions hit after being served that value and the sessions served it (includeLimit, default 10). Those error groups are session-based, so backend-only errors do not appear there; use the FLAG FILTERS breakdown for them.

Do not stop at the first dimension that looks skewed when the conclusion matters; one more breakdown (the version on that host, the routes on that host) usually confirms or breaks it.

5. Evidence

Now pull specific records from the slice step 4 found. Add that slice to every filter (host.name=<host>, the version, the route).

• Logs. search-log-patterns with query: "level=error <slice filter>" groups thousands of lines into a few templates with counts; read those before paging search-logs. Set patternKeys: ["error.message"] when the message is generic and the detail sits in an attribute.
• Traces. search-traces with sortColumn: "duration" and sortDirection: "DESC" for the slowest, or has_errors=true for failed spans. Then get-trace with the traceId and the span’s timestamp to read the tree: which span owns the time, and which child failed first.
• Logs for one request. search-logs with query: "trace_id=<id>" and a window of a few minutes around the span.
• Errors. search-error-groups with the slice filter and sort: "spike" (groups ranked by their count against the previous same-length window, with spike_ratio and new_in_window) or sort: "count". Then get-error-group with the secureId and include: ["distribution", "trend"] for the stack, one occurrence, the spread across browser, OS, environment, service and version, and the occurrence trend with its peak. Its _pivots carry ready get-trace and search-logs arguments for the occurrence. See errors.md.
• Metrics on the slice. For a bad host, search-aggregations with productType: "metrics", query: "host.name=<host>" (add metric_name=*pool*-style words to narrow), groupBy: "metric_name" lists what it reports (pool sizes, CPU, memory). Filter metrics by host, not by the trace service_name: metric data points often carry a different service name, and the filter then matches nothing. Load the observability/metrics guide before reading counters or histograms.

Cite exact identifiers: trace IDs, error group secure IDs, log timestamps, host names, versions.

6. Blast radius

Quantify who and what was affected, over what window.

• search-service-map with service: "<service>" and include: ["health"] gives requests, errors, error rate and p50/p95/p99 for the service and per http.route. Run it for the anomaly window and for the baseline window to compare. Use include: ["dependencies"] to see callers that inherited the failure.
• Users and sessions: search-error-groups with sort: "sessions", or search-aggregations with CountDistinct of a user key such as identifier (sessions) and exact: true.
• Time: first and last bad bucket, using Min and Max of timestamp with bucketCount: 1 (values are Unix seconds; convert them).

Sampling and percentiles

Trace and log aggregations read a sample unless you set exact. When the result says estimated: true, counts are scaled-up estimates and percentiles come from the sampled rows only.

• A percentile over few rows is noise. P99 is roughly the 1 % slowest of the rows actually read: over 100 rows that is the single slowest one, over 600 it is the sixth. When a sampled result has a percentile, search-aggregations adds a low_sample block: a warning, and per expression the buckets whose min_sampled_rows fell below the threshold (P99 under 200 rows, P95 under 50, other percentiles under 20), plus sampled_rows on each percentile bucket. If low_sample is present, do not read a change in that percentile as a regression: rerun with exact: true or fewer, wider buckets. On a server without low_sample, apply the same thresholds yourself: add a Count expression next to the percentile and divide by the sample factor.
• Set exact: true for short windows (a few hours), small or rare counts, distinct counts, and any number you will quote in a conclusion. exact cannot be combined with series; for an exact ratio, run the numerator and denominator as two calls.
• Prefer fewer, wider buckets for percentiles over long windows: 36 two-hour buckets of a sampled P99 swing far more than the underlying latency does.
• includeOther supports only Count, Sum, Min and Max. Rank by Count and report percentiles without it.

Load companion files when relevant

• logs.md: error messages, log patterns, logs for one trace or session
• traces.md: request flow, latency, slow spans, span trees, flag variations on spans
• errors.md: error groups, stack traces, occurrences, regressions
• sessions.md: user journeys and frontend behavior
• metrics.md: search-aggregations options in depth (breakdowns, compareTo, formulas, sampling)
• relationship-search.md: questions about the order, absence or co-occurrence of events in a session or trace (THEN, no_span, AND, OR)

Universal guidance

• Plan, then let each result inform the next. State a short plan before the first call. Re-plan after each step; do not run calls you already know the answer to.
• Aggregate before you read rows. search-aggregations and search-log-patterns answer “how many”, “which ones” and “since when” in one call. search-logs, search-traces, search-sessions and search-error-groups return pages (at most 50 rows per call; follow page.next_cursor or page for more), which is for evidence once you know where to look, not for counting.
• Check that a filter filtered. An invalid clause or an unknown key is dropped, not rejected, and the result gets wider. If a “filtered” count equals the unfiltered one, the filter did nothing. See observability/querying-patterns for the grammar.
• Convert units. Trace duration is in nanoseconds; timestamps from Min/Max are Unix seconds.
• Cross-reference. Trace IDs in logs, sessions on error occurrences, versions on spans: where two products agree, you have evidence.
• Large outputs. When a result is saved to a file because it was too large, extract the slice you need with jq or python3 instead of reading the whole file, and next time narrow the window or lower maxTokens.
• Do not trust a summary over the data. An incident summary, an earlier investigation or a responder’s theory is a hypothesis. Check it with step 4; if the data disagrees, say so and show the numbers.
• On tool failure, try the alternative (a narrower window, a different tool) and report what you found plus the exact next query to run.

Output

End with:

1. Root cause in one or two sentences, with a confidence level, and the numbers that support it (share of failures vs share of traffic, before vs after).
2. Timeline: onset, the change or trigger, peak, recovery, as absolute UTC times.
3. Evidence: trace IDs, error group IDs, log templates with counts, hosts, versions, flag keys.
4. Blast radius: requests, error rate, users or sessions, routes, downstream services.
5. What was ruled out, briefly (for example “version e63885098 also ran on 9 healthy hosts”).
6. Next steps: at least one concrete action (roll back version X, turn flag Y off in production, drain host Z, retune alert N) and any open question with the exact query or data that would answer it.

Related skills

• alert-investigation: start here when an alert fired.
• observability/querying-patterns (hosted MCP guide): the full filter grammar and its silent failures.
• observability/metrics (hosted MCP guide): OpenTelemetry metrics, counters and histograms.
• create-fix-pr: turn the root cause into a pull request.