datadog-error-report

name

datadog-error-report

description

Generate a comprehensive error status report for a service instance using Datadog MCP. Use this skill whenever the user asks about error reports, error status, service health, latency analysis, API performance, incident checks — including Korean phrases like 서비스 현황, 에러 리포트, 장애 현황, latency 분석, API 성능, or even casual requests like 'thomas 어제 에러 좀 봐줘' or 'check alice errors last week'. If the user mentions a service name and wants to know what went wrong, this is the right skill.

Datadog Error Report

Collect error logs, incidents, monitors, events, and API performance metrics from Datadog MCP, classify noise from real errors, and produce a structured report.

Input

The user provides:

• Service name — maps to Datadog service tag (e.g., Thomas, Alice)
• Time range — start/end in KST (end defaults to now)
• Environment — prod/dev/stage (defaults to prod)

Execution

1. Parse parameters

Convert timestamps to milliseconds using Python with KST (UTC+9) — seconds will silently produce wrong results. See references/datadog-queries.md for the conversion snippet.

2. Load Datadog MCP tools

Run 6 ToolSearch calls in parallel before any data queries — the tools aren't available until loaded. See references/datadog-queries.md for the exact tool names.

3. Collect data in parallel (6 queries)

Launch all simultaneously — running them sequentially takes 6x longer for no benefit:

#	Data	Purpose
1	Error logs (pattern mode)	See the big picture of all errors
2	Incidents	Active/resolved incidents for the service
3	Monitors	Alert/warn/OK status
4	Events	Deploys, scaling, incidents
5	p99 latency by endpoint	Find slow APIs
6	TPS / request volume	Understand traffic patterns

See references/datadog-queries.md for exact query templates.

4. Classify error logs (A/B/C)

This is the most important step — without it, infra noise buries real application errors.

Category	What it is	Action
A — Infra noise	JVM/agent artifacts misclassified as errors	Report separately, don't count as app errors
B — Deploy-related	Transient errors during rolling updates	Correlate with deploy events, report separately
C — Application errors	Everything else	Core of the report — analyze in detail

See references/noise-patterns.md for the full pattern list and exclusion filters.

5. Fetch raw logs for Category C

Re-query with noise exclusion filters to get actionable detail on real application errors.

6. Generate report

Use the template in references/report-template.md. The report covers:

1. Error summary with A/B/C classification
2. Category C detail with frequency, time distribution, and causal chain analysis
3. Incidents, monitors, events
4. API performance (TPS, p99, slow APIs)
5. Prioritized recommendations

7. Deep links

When the user wants to drill into specific errors, construct Datadog URLs using facet queries (@error.message:*keyword*), not full-text search — special characters break the UI. See references/datadog-queries.md for the URL format.