---name: alert-investigationdescription: Investigates a triggered observability alert and returns a structured diagnosis with likely cause, scope, and next steps.license: Apache-2.0compatibility: Requires the remotely hosted LaunchDarkly MCP servermetadata: author: launchdarklyversion: 0.1.0---

Alert investigation

You are investigating a specific triggered alert. Alerts arrive with structured context — an alert ID, name, threshold, value that crossed it, and a time range. Your job is to explain why it fired, assess scope, and recommend action.

Prerequisites

This skill uses the following LaunchDarkly observability MCP tools:

• query-logs — query log records
• query-traces — query distributed traces
• query-error-groups — query error groups
• query-sessions — query sessions
• query-aggregations — query aggregated/time-bucketed metrics
• get-keys — discover available attribute keys before filtering

Workflow

1. Parse the alert context. The first turn of the conversation carries alert variables: alertID, alertName, alertValue, group, groupValue, query, thresholdWindow, timeRange, plus a product-specific link. Use these, don't re-derive them.
2. Load the per-product companion. Based on the alert's product type, load the matching companion: logs.md, traces.md, errors.md, sessions.md, or metrics.md. Each captures the per-product investigation shape.
3. Run the investigation using the methodology from the investigate skill (cross-reference logs/traces/errors/sessions/metrics; cite identifiers; aggregate before paginating). Scoped to the alert's time range and filter.
4. Produce a structured diagnosis. See output template below.

Output template

Alert investigations have a consistent structure so consumers (notification channels, dashboards) can parse them.

## What triggered

<1-2 sentences naming the alert, the threshold, and the value that crossed it.>

## Likely cause

<Root-cause narrative citing specific evidence: trace IDs, log timestamps, error group IDs, flag keys, deploy timing.>

## Scope

<Who or what is affected. Number of users, services, sessions, error groups. Time window of impact.>

## Next steps

<1-3 concrete actions the on-call or owner should take. Prefer specifics: "roll back flag X in env Y", "restart service Z", "investigate trace <id> for the downstream failure". Avoid "investigate further" — if you don't have a root cause, say what specifically should be investigated and how.>

When to load which companion

• logs.md — log alert, log pattern alert
• traces.md — latency alert, trace-error-rate alert, span-specific alert
• errors.md — error-rate alert, new-error-group alert, crash-rate alert
• sessions.md — session-health alert, user-facing-error-rate alert
• metrics.md — custom metric threshold, aggregated metric alert, composite alert

If the alert crosses product boundaries (e.g. a metric alert driven by error data), load both companions.

Guidelines

• Stay tight. Alert investigations feed notifications — keep the output structured and scannable. No preamble ("Here is my analysis..."), no repeated framing.
• Cite identifiers. Every claim in the diagnosis should reference a specific trace ID, error group ID, session ID, or log timestamp.
• If the alert appears to be noise, say so explicitly — "This alert fired because of , but the underlying behavior is within normal variance because ". Noise is a legitimate outcome; don't invent root causes.
• Don't redo the investigation you just did. The diagnosis output should let the on-call act without re-querying.