How to build Shortcut dashboards in Metabase

Shortcut is a project management tool for software teams built around stories, epics, iterations, and lightweight workflows. 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 Shortcut MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs Shortcut history into a database so you can build dashboards anyone can read.

How do you connect Shortcut 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 Shortcut data in Metabase?

Once stories and the related epics, iterations, workflow states data are in a database, the highest-value views are:

• Cycle and lead time — how fast stories move once started, and how long requesters wait end to end.
• Iteration velocity and carryover — committed vs. completed points, and how much spills into the next iteration.
• Bug inflow vs. resolution — whether defects are being fixed faster than they arrive.
• WIP and blocked stories — work in progress by workflow state, and what's stuck right now.
• Epic progress — completion and remaining scope for the bets that span multiple iterations.

Which Shortcut dashboards should you build in Metabase?

How do you use the Shortcut MCP Server with the Metabase CLI?

Pair the Shortcut 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 completed stories from the last few iterations with state timestamps.
• 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.
• Story state-change history is required for true cycle time, carryover, and time-in-state.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

{
  "mcpServers": {
    "shortcut": {
      "url": "https://mcp.shortcut.com/mcp"
    }
  }
}

Shortcut archived its GitHub MCP repository in July 2026 — the hosted endpoint is the supported path; don't install the old local package.

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

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

Can you generate a Shortcut dashboard with AI?

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

Goal: Help engineering leaders understand cycle and lead time, iteration health, epic progress, and bug quality from Shortcut data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for shortcut tables and
  models). If durable Shortcut 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 Shortcut MCP Server:
  stories, plus epics, iterations, workflow states.
  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
  Shortcut — 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: Shortcut Issue Tracking Overview

Sections:
1. Executive summary: Stories completed last 30 days; Median cycle time;
   Carryover rate; Open bugs; Blocked stories.
2. Flow: Cycle and lead time trends; time in state; blocked work.
3. Iterations: Committed vs completed; carryover; mid-iteration scope change.
4. Epics: Completion by epic; stale epics; remaining scope.
5. Quality: Bug inflow vs resolution; bug aging; high-priority bugs.

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 Shortcut data into a database or warehouse?

For dashboards that need history and reliability, land Shortcut 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 Shortcut REST API v3 for control over fields, grain, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Airbyte's marketplace Shortcut source syncs roughly two dozen streams — stories, epics, iterations, workflows, members, milestones, and labels. Fivetran has no Shortcut connector, so Airbyte or a dlt/API script is the pipeline route.

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 team, story type, workflow state, estimate, and state-transition-timestamp fields.

How should you model Shortcut data in Metabase?

Core tables

Modeling advice

• Build a clean stories 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 Shortcut metrics should you track in Metabase?

What SQL powers Shortcut 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', completed_at) AS week,
  COUNT(*) AS stories_completed,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (completed_at - started_at)) / 86400
  ) AS median_cycle_days
FROM stories
WHERE completed_at IS NOT NULL
  AND started_at IS NOT NULL
GROUP BY 1
ORDER BY 1;

SELECT
  i.name AS iteration,
  i.points_committed,
  i.points_done,
  ROUND(
    100.0 * (i.points_committed - i.points_done)
    / NULLIF(i.points_committed, 0), 1
  ) AS carryover_pct
FROM iterations i
WHERE i.end_date >= CURRENT_DATE - INTERVAL '120 days'
ORDER BY i.start_date;

-- Created and resolved use different time axes so the combo chart
-- compares inflow vs. throughput, not "bugs created this week that later closed."
WITH created AS (
  SELECT
    date_trunc('week', created_at) AS week,
    COUNT(*) AS bugs_created
  FROM stories
  WHERE type = 'bug'
  GROUP BY 1
),
resolved AS (
  SELECT
    date_trunc('week', completed_at) AS week,
    COUNT(*) AS bugs_resolved
  FROM stories
  WHERE type = 'bug'
    AND completed_at IS NOT NULL
  GROUP BY 1
)
SELECT
  COALESCE(c.week, r.week) AS week,
  COALESCE(c.bugs_created, 0) AS bugs_created,
  COALESCE(r.bugs_resolved, 0) AS bugs_resolved
FROM created c
FULL OUTER JOIN resolved r ON r.week = c.week
ORDER BY 1;

What are common mistakes when analyzing Shortcut in Metabase?

Related

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FAQ