How to build Jenkins dashboards in Metabase

Jenkins is the open-source automation server that runs the build, test, and deployment pipelines behind a huge share of the world's software. Metabase is where you turn that delivery activity into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Jenkins MCP Server plugin and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Jenkins history into a database so you can build dashboards anyone can read.

How do you connect Jenkins to Metabase?

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

What can you analyze from Jenkins data in Metabase?

Once builds (pipeline runs) and the related jobs, stages, test results data are in a database, the highest-value views are:

• Build success rate by job — which jobs fail most, and whether main-branch stability is improving week over week.
• Pipeline duration trend — median and p95 build times, so a slowly degrading feedback loop shows up before people complain.
• Flaky test hotspots — tests that pass and fail on the same code, ranked by how much rerun time they burn.
• Queue time and agent utilization — how long builds wait for an executor, and whether the agent fleet matches demand.
• Deployment pipeline health — success rate and duration for the jobs that actually ship to production.

Which Jenkins dashboards should you build in Metabase?

How do you use the Jenkins MCP Server plugin with the Metabase CLI?

Pair the Jenkins MCP Server plugin 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 the last 90 days of builds (pipeline runs) with status, duration, and queue time.
• Export the result as CSV, keeping stable IDs, projects, states, 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.
• Run-grain history with queued, started, and finished timestamps is required for duration and success-rate trends.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "jenkins": {
      "url": "https://jenkins.example.com/mcp-server/mcp"
    }
  }
}

Requires Jenkins 2.533+ with the official mcp-server plugin installed — the endpoint lives on your own controller. Several community PyPI/npm packages use Jenkins-flavored names; the plugin from the jenkinsci org is the official one.

# 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 builds-pipeline-runs export — creates a table AND a model
mb upload csv --file jenkins-builds-pipeline-runs.csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file jenkins-builds-pipeline-runs.csv

Can you generate a Jenkins dashboard with AI?

Yes. Use the prompt below with any assistant that can run the Jenkins MCP Server plugin and the Metabase CLI. It works end to end: if Jenkins 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 Jenkins ci/cd analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help engineering leaders understand build health, pipeline performance, test quality, and delivery throughput from Jenkins data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for jenkins tables and
  models). If durable Jenkins 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 Jenkins MCP Server plugin:
  builds (pipeline runs), plus jobs, stages, test results.
  Prefer entity tables and rollups over raw logs. 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, projects,
branches, states, and timestamps, and whether history exists before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Jenkins — it reads a database or CLI-uploaded tables.
- Never try to load raw build logs or event streams into Metabase; use runs,
  entities, state changes, and rollups.
- Only compute durations (cycle time, build duration, time to review) when the
  required timestamps exist.
- Exclude bot activity, draft work, and non-production environments from
  headline numbers, and keep the segmenting column explicit.
- 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: Jenkins CI/CD Overview

Sections:
1. Executive summary: Builds last 30 days; Success rate; Median duration;
   Flaky tests flagged; Production deploys.
2. Build health: Success rate by week; failed builds by job; builds per day.
3. Performance: Median and p95 duration by week and pipeline; queue time.
4. Test quality: Flaky tests by failure rate; test failures per week; retries.
5. Throughput: Deploys per week; change failure rate; lead time where synced.

Filters: Date range, Project or Repository, Branch or Environment, 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 Jenkins data into a database or warehouse?

For dashboards that need history and reliability, land Jenkins entities and change history 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 Jenkins remote access API for control over fields, grain, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

There is no reliable managed connector for Jenkins — Airbyte's source is archived. Most teams script exports against the remote access API (builds, jobs, test reports as JSON), or push build events into a database from the pipeline itself with a post-build step.

Notes

• Land raw entity tables first, then build clean Metabase models on top.
• Sync state-change or audit history from day one — duration metrics can't be reconstructed later without it.
• Normalize project or job, branch, status, queued/started/finished-timestamp, and duration fields.

How should you model Jenkins data in Metabase?

Core tables

Modeling advice

• Build a clean pipeline_runs model with common columns across tools, so multi-source dashboards don't fork definitions.
• Separate entity tables (projects, repos, flags, members) from run-grain and event-grain tables.
• Exclude bots, drafts, and non-production environments from headline metrics; keep the flag as an explicit column.
• Use stable IDs for project, repo, and person joins; display names change.

Which Jenkins metrics should you track in Metabase?

What SQL powers Jenkins 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('week', started_at) AS week,
  COUNT(*) AS builds,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE result = 'success')
    / NULLIF(COUNT(*), 0), 1
  ) AS success_rate_pct
FROM pipeline_runs
WHERE branch = 'main'
GROUP BY 1
ORDER BY 1;

SELECT
  pipeline_name,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY duration_seconds
  ) / 60.0 AS median_minutes,
  percentile_cont(0.95) WITHIN GROUP (
    ORDER BY duration_seconds
  ) / 60.0 AS p95_minutes
FROM pipeline_runs
WHERE started_at >= CURRENT_DATE - INTERVAL '30 days'
  AND result = 'success'
GROUP BY pipeline_name
ORDER BY median_minutes DESC;

SELECT
  test_name,
  COUNT(*) AS runs,
  COUNT(*) FILTER (WHERE result = 'failed') AS failures,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE result = 'failed')
    / NULLIF(COUNT(*), 0), 1
  ) AS failure_rate_pct
FROM test_results
WHERE run_at >= CURRENT_DATE - INTERVAL '14 days'
GROUP BY test_name
HAVING COUNT(*) FILTER (WHERE result = 'failed') > 0
  AND COUNT(*) FILTER (WHERE result = 'passed') > 0
ORDER BY failure_rate_pct DESC
LIMIT 20;

What are common mistakes when analyzing Jenkins in Metabase?

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FAQ