ClickUp × Metabase

How to build ClickUp dashboards in Metabase

ClickUp is a all-in-one productivity platform for tasks, docs, chat, goals, whiteboards, and teams. Metabase is where you turn that operational activity into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI routethat pulls live data with the ClickUp MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that syncs ClickUp into a database so you can build dashboards anyone can read.

Heads up: Metabase connects to databases and warehouses — it does not ship a native ClickUp connector. For dashboards that need history and reliability, sync ClickUp into a database first.

How do you connect ClickUp to Metabase?

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

1 · MCP + CLI route (AI-assisted)

Live data in, quick analysis out

Pair the ClickUp MCP Server with the Metabase CLI. Use MCP for live lookups, write the result to CSV, then load it into Metabase as a ready-to-query table and model.

Best for
  • Quick lookups such as "show me space and list throughput"
  • Loading a ClickUp CSV export into Metabase in seconds
  • Spot-checks and one-off analyses without a warehouse
Trade-offs
  • Great for exploration, not governed reporting
  • Use read-only/scoped credentials wherever the MCP server supports them
  • CSV uploads are snapshots — refresh or move to the pipeline for history
2 · Pipeline route (warehouse-backed)

Durable dashboards with history

Sync ClickUp into a database or warehouse with a managed connector, custom pipeline, or API, then point Metabase at it.

Best for
  • ClickUp dashboards leaders depend on
  • Joining ClickUp data with product, support, sales, or engineering data
  • Long-run trends for space and list throughput and status aging
Trade-offs
  • You own the refresh schedule and clean data model
  • Change and activity history need explicit snapshots, events, or changelog syncs
  • Metric definitions must be consistent across tools and teams

What can you analyze from ClickUp data in Metabase?

  • Space and list throughput — built from tasks and the related spaces, folders, lists data your sync exposes.
  • Status aging — built from tasks and the related spaces, folders, lists data your sync exposes.
  • Workload and capacity — built from tasks and the related spaces, folders, lists data your sync exposes.
  • Overdue work by list — built from tasks and the related spaces, folders, lists data your sync exposes.
  • Goal and milestone progress — built from tasks and the related spaces, folders, lists data your sync exposes.

Which ClickUp dashboards should you build in Metabase?

For: Team leads

Work overview

A shared pulse of active, completed, and delayed work.

  • tasks completed by week (line)
  • Created vs completed by week (combo)
  • Open work by status (stacked bar)
  • Overdue work (number + trend)
For: Program owners

Project health

Which projects need attention before they slip?

  • Project health score (table)
  • At-risk projects by owner (bar)
  • Blocked or stale work (table)
  • Deadline risk by project (heatmap)
For: Managers

Workload balance

Work distribution without turning people into a scoreboard.

  • Open work by owner (bar)
  • Overdue work by owner (bar)
  • Work in progress by team (stacked bar)
  • Unassigned work (number + table)
For: Operations

Flow and bottlenecks

Where work waits, ages, or piles up.

  • Median cycle time by week (line)
  • Time in status (bar)
  • Aging work by status (table)
  • Throughput by work type (bar)

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

Pair the ClickUp MCP Server with the Metabase CLI for fast, hands-on analysis. MCP is useful for scoped lookups and 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 overdue or recently completed tasks.
  • Export the result as CSV, keeping stable IDs, owners, containers, status or type, 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.
  • Status history is required for true cycle time and time-in-status metrics.
  • mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

ClickUp MCP Serverofficial

Transport
Hosted remote MCP via Streamable HTTP (public beta)
Auth
OAuth through ClickUp
Best for
Live scoped lookup and export

Metabase CLIofficial

Install
npm install -g @metabase/cli
Auth
mb auth login
Load data
mb upload csv --file data.csv
Requires
An uploads database (Admin → Settings → Uploads)
MCPExample MCP client config
{
  "mcpServers": {
    "clickup": {
      "url": "https://mcp.clickup.com/mcp"
    }
  }
}
TerminalLoad a ClickUp CSV with the Metabase CLI
# 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 tasks export — creates a table AND a model
mb upload csv --file clickup-tasks.csv --collection root

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

Can you generate a ClickUp dashboard with AI?

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

Prompt for creating a ClickUp Work Management Overview dashboard
Create a polished Metabase dashboard for ClickUp work management analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help team and program leaders understand completion, throughput, overdue work, workload balance, and project health from ClickUp data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for clickup tables and
  models). If durable ClickUp 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 export with the ClickUp MCP Server: tasks,
  spaces, folders, lists. 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, dates, owners,
containers, status values, and whether change/activity history exists before creating
duration or trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  ClickUp — it reads a database or CLI-uploaded tables.
- Only compute durations when the required start/end or event timestamps exist.
- Exclude archived, deleted, canceled, or test objects from active-population cards.
- Avoid individual performance leaderboards; use owner views for balancing and cleanup.
- 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: ClickUp Work Management Overview

Sections:
1. Executive summary: Completed work last 30 days; Open work; Overdue rate;
   Median cycle time if available; Unassigned work.
2. Throughput: Created vs completed by week; Completed work by type/status/project.
3. Project health: At-risk projects; blocked work; stale work; upcoming deadlines.
4. Workload: Open and overdue work by owner/team; WIP by status.
5. Flow: Cycle time, lead time, and time in status if history is available.

Filters: Date range, Project/board/database, Status, Owner, Team, Work type.

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 ClickUp data into a database or warehouse?

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

Sync spaces, folders, lists, tasks, statuses, users, time estimates, comments, and custom fields with the API or a managed ELT connector.

Notes

  • Land raw tables first, then build clean Metabase models on top.
  • Keep change events or daily snapshots if you need trends and duration metrics.
  • Normalize owner, team, project, status, due-date, and completed-date fields.

How should you model ClickUp data in Metabase?

Core tables

TableGrainKey columns
clickup_tasksone row per taskid, name, list_id, status, assignee_id, due_date, date_done
clickup_listsone row per listid, name, folder_id, space_id, archived
clickup_commentsone row per commentid, task_id, user_id, comment_text, created_at

Modeling advice

  • Build a clean work_items model with common columns across tools.
  • Separate current-state fields from change events, activity, or snapshot history.
  • Exclude archived, deleted, test, and duplicate objects from active-population metrics.
  • Use stable IDs for owner, project, and item joins; display names can change.

Which ClickUp metrics should you track in Metabase?

MetricDefinitionNotes
Task completion rateCompleted items ÷ items due or committed in the period.Define the denominator once.
Task throughputCompleted items per period.Segment by project, type, or team.
Overdue rateOpen items past due ÷ open items with due dates.Watch missing due dates separately.
Workload balanceDistribution of open work across owners or teams.Use for capacity, not ranking.
Project health scoreWeighted risk from overdue, blocked, stale, and incomplete work.Keep the formula visible.

What SQL powers ClickUp dashboards in Metabase?

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

Completed work by weekPostgreSQL

Basic task throughput.

SELECT
  date_trunc('week', completed_at) AS week,
  COUNT(*) AS completed_items
FROM work_items
WHERE completed_at IS NOT NULL
  AND archived_at IS NULL
GROUP BY 1
ORDER BY 1;
Overdue rate by projectPostgreSQL

Open work past due, divided by open work.

SELECT
  project_name,
  COUNT(*) FILTER (
    WHERE completed_at IS NULL
      AND due_date < CURRENT_DATE
  ) AS overdue_items,
  COUNT(*) FILTER (
    WHERE completed_at IS NULL
      AND due_date IS NOT NULL
  ) AS open_items,
  ROUND(
    100.0 * COUNT(*) FILTER (
      WHERE completed_at IS NULL
        AND due_date < CURRENT_DATE
    ) / NULLIF(COUNT(*) FILTER (
      WHERE completed_at IS NULL
        AND due_date IS NOT NULL
    ), 0),
    2
  ) AS overdue_rate
FROM work_items
WHERE archived_at IS NULL
GROUP BY project_name
ORDER BY overdue_rate DESC NULLS LAST;
Open and overdue work by ownerPostgreSQL

A capacity view, not a performance leaderboard.

SELECT
  owner_name,
  COUNT(*) FILTER (WHERE completed_at IS NULL) AS open_items,
  COUNT(*) FILTER (
    WHERE completed_at IS NULL
      AND due_date < CURRENT_DATE
  ) AS overdue_items
FROM work_items
WHERE archived_at IS NULL
GROUP BY owner_name
ORDER BY open_items DESC;

What are common mistakes when analyzing ClickUp in Metabase?

Treating current-state exports as history.→ Keep snapshots or changelogs if you need trends, cycle time, or time in status.
Counting archived or canceled work as incomplete.→ Define which statuses count as active, completed, canceled, and excluded.
Comparing people with raw item counts.→ Use workload views for balancing capacity; item counts ignore size and complexity.
Mixing teams with different workflows in one metric.→ Normalize statuses into common groups before comparing project health.
Building dashboards from live MCP lookups only.→ MCP is useful for exploration; durable dashboards need a database-backed model.

Related analytics

Related integrations

FAQ

Does Metabase connect natively to ClickUp?
No. Metabase reads databases and warehouses. Sync ClickUp into a database first, or upload a CSV with the Metabase CLI, then build Metabase models and dashboards on top.
Is the ClickUp MCP route enough for dashboards?
Use MCP for live exploration and quick exports. For dashboards people depend on, sync the data into a database or warehouse so you keep history, refresh schedules, permissions, and a governed model layer.
Can I measure cycle time from current-state exports?
Usually no. True cycle time needs a reliable started timestamp or status-change history. Without it, report lead time from created to done and caveat the gap.
How should I compare workload by person?
Use owner-level views to spot load-balancing problems and unassigned work, not to rank individuals. Work size, type, interruptions, and hidden review effort vary too much for a fair scoreboard.