How to build Checkly dashboards in Metabase

Checkly is a synthetic monitoring platform for API checks, browser checks, and Playwright-powered end-to-end monitoring as code. 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 Checkly MCP server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Checkly rollups into a database so you can build dashboards anyone can read.

How do you connect Checkly 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 Checkly data in Metabase?

• Check success rate and uptime — built from check result rollups and the related checks, check groups, alerts data your sync exposes.
• API and browser check latency — built from check result rollups and the related checks, check groups, alerts data your sync exposes.
• Failing checks and flakiness — built from check result rollups and the related checks, check groups, alerts data your sync exposes.
• Alert volume by check — built from check result rollups and the related checks, check groups, alerts data your sync exposes.
• Check coverage by service — built from check result rollups and the related checks, check groups, alerts data your sync exposes.

Which Checkly dashboards should you build in Metabase?

How do you use the Checkly MCP server with the Metabase CLI?

Pair the Checkly 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 check result rollups with downtime incidents and response-time summaries.
• 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.
• Per-window check rollups and incident start/resolve timestamps are required for uptime and MTTR trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

How do you set up Checkly MCP and the Metabase CLI?

{
  "mcpServers": {
    "checkly": {
      "url": "https://api.checklyhq.com/mcp"
    }
  }
}

API-key auth needs both an Authorization key and an X-Checkly-Account header. For creating and deploying check code, Checkly points you at its CLI instead — the MCP server is for querying.

# 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 check-result-rollups export — creates a table AND a model
mb upload csv --file checkly-check-result-rollups.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file checkly-check-result-rollups.csv

Can you generate a Checkly dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Checkly MCP server and the Metabase CLI. It works end to end: if Checkly 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 Checkly uptime monitoring 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 uptime by monitor, downtime incidents, recovery speed, response times, and SLA compliance from Checkly data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for checkly tables and
  models). If durable Checkly 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 Checkly MCP server:
  check result rollups, plus checks, check groups, alerts.
  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
  Checkly — 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: Checkly Uptime Monitoring Overview

Sections:
1. Executive summary: Overall uptime last 30 days; Monitors below target;
   Downtime incidents last 30 days; Median time to recovery.
2. Uptime: Uptime percentage by monitor by week; monitors below target.
3. Incidents: Downtime incidents by week; recovery-time trend; longest outages.
4. Response time: p95 by monitor; slowest endpoints; degradation vs. baseline.
5. SLA: Compliance by service; downtime minutes vs. allowance; breach history.

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 Checkly data into a database or warehouse?

For dashboards that need history and reliability, land Checkly 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 Checkly Analytics API for control over rollup grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Sync check results with the Fivetran Checkly connector (a community Connector-SDK example), or script the Checkly Analytics API for aggregated metrics — percentiles, error rates, and grouping by location — per check.

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 monitor, service, environment, window-start, up-seconds, and incident-timestamp fields.

How should you model Checkly data in Metabase?

Core tables

Modeling advice

• Build a clean uptime_check_rollups 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 Checkly metrics should you track in Metabase?

What SQL powers Checkly dashboards in Metabase?

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

SELECT
  monitor_name,
  date_trunc('week', window_start) AS week,
  ROUND(
    100.0 * SUM(up_seconds) / NULLIF(SUM(total_seconds), 0), 3
  ) AS uptime_pct
FROM uptime_check_rollups
WHERE environment = 'production'
GROUP BY 1, 2
ORDER BY 1, 2;

SELECT
  monitor_name,
  COUNT(*) AS downtime_incidents,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (resolved_at - started_at)) / 60
  ) AS median_recovery_minutes,
  MAX(EXTRACT(EPOCH FROM (resolved_at - started_at)) / 60)
    AS longest_outage_minutes
FROM downtime_incidents
WHERE started_at >= CURRENT_DATE - INTERVAL '90 days'
  AND resolved_at IS NOT NULL
GROUP BY monitor_name
ORDER BY downtime_incidents DESC;

-- Keep average and p95 at the rollup grain they were computed.
-- Re-averaging avgs or taking MAX of daily p95s invents a fake percentile.
SELECT
  monitor_name,
  window_start,
  avg_response_ms,
  p95_response_ms
FROM uptime_check_rollups
WHERE window_start >= CURRENT_DATE - INTERVAL '28 days'
ORDER BY 1, 2;

What are common mistakes when analyzing Checkly in Metabase?

Related

Related analytics

Related dashboards

Related integrations

FAQ