How to build Kibana dashboards in Metabase

Kibana is the visualization and management layer of the Elastic Stack for dashboards, alerting rules, and cases. 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 Elastic Agent Builder MCP (via Kibana) and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Kibana rollups into a database so you can build dashboards anyone can read.

How do you connect Kibana 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 Kibana data in Metabase?

• Case volume and resolution time — built from cases and the related alerting rules, rule executions, dashboards data your sync exposes.
• Alert rule health and noise — built from cases and the related alerting rules, rule executions, dashboards data your sync exposes.
• Rule execution failures — built from cases and the related alerting rules, rule executions, dashboards data your sync exposes.
• Detection coverage by space — built from cases and the related alerting rules, rule executions, dashboards data your sync exposes.
• Dashboard inventory and ownership — built from cases and the related alerting rules, rule executions, dashboards data your sync exposes.

Which Kibana dashboards should you build in Metabase?

How do you use the Elastic Agent Builder MCP (via Kibana) with the Metabase CLI?

Pair the Elastic Agent Builder MCP (via Kibana) 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 cases 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 Kibana MCP and the Metabase CLI?

{
  "mcpServers": {
    "kibana": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote",
        "https://your-kibana.example.com/api/agent_builder/mcp",
        "--header", "Authorization: ApiKey ${ES_API_KEY}"
      ]
    }
  }
}

For saved-object inventory (dashboards, visualizations), the community @tocharianou/mcp-server-kibana server covers the Kibana management APIs.

# 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 cases export — creates a table AND a model
mb upload csv --file kibana-cases.csv --collection root

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

Can you generate a Kibana dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Elastic Agent Builder MCP (via Kibana) and the Metabase CLI. It works end to end: if Kibana 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 Kibana 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 Kibana data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for kibana tables and
  models). If durable Kibana 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 Elastic Agent Builder MCP (via Kibana):
  cases, plus alerting rules, rule executions, dashboards.
  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
  Kibana — 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: Kibana 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 Kibana data into a database or warehouse?

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

No managed connector exists — script the Kibana REST APIs for alerting rules, rule executions, cases, and saved-object inventory; the underlying log data syncs via the Elasticsearch connectors.

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 Kibana 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 Kibana metrics should you track in Metabase?

What SQL powers Kibana 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,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (acknowledged_at - created_at)) / 60
  ) AS median_ack_minutes,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (resolved_at - created_at)) / 60
  ) AS median_resolve_minutes
FROM incidents
WHERE resolved_at IS NOT NULL
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 Kibana in Metabase?

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FAQ