How to build incident.io dashboards in Metabase

incident.io is an incident management platform for declaring, running, and learning from incidents — with on-call, status pages, and post-incident workflows built in. Metabase is where you turn those operational signals into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the incident.io MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs incident.io rollups into a database so you can build dashboards anyone can read.

How do you connect incident.io to Metabase?

Most teams combine both routes: use MCP and CLI uploads for a fast first pass, then move recurring reliability reporting to a warehouse-backed model.

What can you analyze from incident.io data in Metabase?

• Incident health with MTTA and MTTR — built from incidents and the related follow-ups, escalations, incident roles data your sync exposes.
• Incident volume by severity and type — built from incidents and the related follow-ups, escalations, incident roles data your sync exposes.
• Follow-up completion and aging — built from incidents and the related follow-ups, escalations, incident roles data your sync exposes.
• On-call and escalation load — built from incidents and the related follow-ups, escalations, incident roles data your sync exposes.
• Status page communication cadence — built from incidents and the related follow-ups, escalations, incident roles data your sync exposes.

Which incident.io dashboards should you build in Metabase?

How do you use the incident.io MCP server with the Metabase CLI?

Pair the incident.io MCP server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and summarized exports; the Metabase CLI's upload command loads CSV data into Metabase and creates a ready-to-query table and model.

Example workflow

• Ask the MCP server for recent incidents with acknowledge and resolve timestamps.
• Export the result as CSV, keeping stable IDs, services, environments, severities, and timestamps.
• Run mb upload csv to load it into Metabase as a table and model, then build questions and dashboards on top.

Be honest about the limits

• MCP lookups are excellent for exploration, not scheduled reporting.
• A CSV upload is a snapshot; refresh it with mb upload replace or move to the pipeline for real history.
• Acknowledge and resolve timestamps are required for true MTTA and MTTR.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up incident.io MCP and the Metabase CLI?

{
  "mcpServers": {
    "incident-io": {
      "url": "https://mcp.incident.io/mcp"
    }
  }
}

Public Beta on Team, Pro, and Enterprise plans; enable it under Settings → MCP. The incident_stats, alert_stats, and escalation_stats tools return aggregates directly — handy for analytics exports.

# Install the Metabase CLI
npm install -g @metabase/cli

# Log in (opens your browser; requires Metabase v62+)
mb auth login --url https://your-metabase.example.com

# Load a incidents export — creates a table AND a model
mb upload csv --file incident-io-incidents.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file incident-io-incidents.csv

Can you generate a incident.io dashboard with AI?

Yes. Use the prompt below with any assistant that can run the incident.io MCP server and the Metabase CLI. It works end to end: if incident.io tables already exist in Metabase it analyzes those; otherwise it pulls scoped, summarized data over MCP, loads it with mb upload csv, then builds the dashboard and caveats any metric that needs missing history.

Create a polished Metabase dashboard for incident.io incident management analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering and operations leaders understand incident volume, MTTA, MTTR, alert noise, on-call load, and service reliability from incident.io data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for incident-io tables and
  models). If durable incident.io data is already present — synced from a warehouse
  or uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the incident.io MCP server:
  incidents, plus follow-ups, escalations, incident roles.
  Prefer aggregated or rollup views over raw events. Write each result to a CSV,
  then load it with the Metabase CLI — run "mb upload csv --file <export>.csv" so
  each upload creates a table and a ready-to-query model. Use "mb upload replace
  <table-id> --file <export>.csv" to refresh an existing table instead of creating
  duplicates.

Step 2 — Inspect before querying:
Do not assume exact table or column names. Inspect available fields, services,
environments, timestamps, and whether rollups or history exist before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  incident.io — it reads a database or CLI-uploaded tables.
- Never try to load the raw event firehose into Metabase; use rollups, entity
  tables, and incident- or group-grain data.
- Only compute durations (MTTA, MTTR, time-to-resolve) when the required
  timestamps exist.
- Exclude test, staging, or muted objects from headline reliability cards, and
  segment by environment where the field exists.
- A single CSV is a point-in-time snapshot: only build trend cards if there is a
  usable date column or multiple periods have been uploaded.

Dashboard title: incident.io Incident Management Overview

Sections:
1. Executive summary: Open incidents; Incidents last 30 days; Median MTTA;
   Median MTTR; High-urgency share.
2. Volume: Incidents by week and severity; incidents by service and team.
3. Response: MTTA and MTTR trends; incidents breaching response targets.
4. Alert quality: Alerts by week; alert-to-incident conversion; noisiest services.
5. On-call load: Pages per shift; off-hours pages; escalation rate.

Filters: Date range, Service, Environment, Severity, Team, Status.

Output: Build the dashboard if you have permission; otherwise provide the exact
questions, SQL, model definitions, and layout. Include caveats for any metric
that cannot be calculated from the available data.

How do you sync incident.io data into a database or warehouse?

For dashboards that need history and reliability, land incident.io rollups and metadata in a database first, then connect Metabase to that database.

Connector options

• Managed ETL — use a connector when one covers the objects you need.
• Custom pipeline — use the incident.io API for control over rollup grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync incidents, follow-ups, and metadata with the Airbyte incident.io source or a dlt pipeline against the incident.io API — both authenticate with an API key.

Notes

• Decide the rollup grain first (hourly or daily per service/environment) — it drives warehouse cost and every trend card.
• Land raw entity tables first, then build clean Metabase models on top.
• Normalize service, team, severity, created, acknowledged, and resolved-timestamp fields.

How should you model incident.io data in Metabase?

Core tables

Modeling advice

• Build a clean incidents model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (services, monitors, policies) from time-series rollups and event-grain tables.
• Exclude test, staging, and muted objects from headline reliability metrics; keep environment as an explicit column.
• Use stable IDs for service, team, and incident joins; display names change.

Which incident.io metrics should you track in Metabase?

What SQL powers incident.io dashboards in Metabase?

These assume a cleaned analytical model in a warehouse (PostgreSQL dialect). Adjust table and column names to match your pipeline.

SELECT
  date_trunc('month', created_at) AS month,
  COUNT(*) AS incidents,
  COUNT(*) FILTER (WHERE resolved_at IS NOT NULL) AS resolved_incidents,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (acknowledged_at - created_at)) / 60
  ) FILTER (WHERE acknowledged_at IS NOT NULL) AS median_ack_minutes,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (resolved_at - created_at)) / 60
  ) FILTER (WHERE resolved_at IS NOT NULL) AS median_resolve_minutes
FROM incidents
GROUP BY 1
ORDER BY 1;

SELECT
  service_name,
  COUNT(*) AS alerts,
  COUNT(*) FILTER (WHERE became_incident) AS incidents,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE NOT became_incident)
    / NULLIF(COUNT(*), 0), 1
  ) AS noise_rate
FROM alerts
WHERE fired_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY service_name
ORDER BY noise_rate DESC;

SELECT
  service_name,
  severity,
  COUNT(*) AS incidents,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (resolved_at - created_at)) / 3600
  ) AS median_resolve_hours
FROM incidents
WHERE created_at >= CURRENT_DATE - INTERVAL '90 days'
  AND resolved_at IS NOT NULL
GROUP BY service_name, severity
ORDER BY incidents DESC;

What are common mistakes when analyzing incident.io in Metabase?

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FAQ