How to build Drag inbox dashboards in Metabase

{
  "mcpServers": {
    "drag": {
      "command": "npx",
      "args": ["-y", "@dragapp/mcp-server"],
      "env": {
        "DRAG_API_KEY": "your-drag-api-key"
      }
    }
  }
}

# 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 Drag CSV export — creates a table AND a model
mb upload csv --file drag-cards.csv --collection root

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

Create a polished Metabase dashboard for Drag shared-inbox analytics.
Work end to end: get the data into Metabase if it isn't there yet, then build.

Goal: Help team leads understand inbox volume, response time, workload, and tag
drivers from Drag (Gmail shared inbox) data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Drag tables and
  models). If durable Drag data is already present — synced from a warehouse or
  uploaded earlier — use it and skip to Step 2.
- If nothing is there, pull it with the Drag MCP server (keep to its read-only
  tools): boards, cards, columns, emails, tags, users, and contacts. 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 names. Map the available raw tables into these
analytical concepts where possible: Boards (shared inboxes), Cards
(conversations), Columns (statuses), Threads/Emails, Tags, Users, and Contacts.
Inspect the actual tables and column names first.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Drag — it reads a database or CLI-uploaded tables.
- Use medians (p50) and p90 for reply times, never averages.
- Define "first reply" as the first outbound email from a team member, excluding
  internal notes and automated messages.
- If card movement history is missing, do not calculate time-in-column or reopen
  rate. Use a caveat instead.
- Only build a card if its underlying column/metric exists in the data.
- A single CSV is a point-in-time snapshot: trends and time-in-column need
  history, so build trend cards only if a warehouse sync or multiple uploads
  provide it.

Dashboard title: Drag Inbox Overview

Sections:
1. Executive summary (KPI cards): Cards created last 7 days; Closed last 7 days;
   Open backlog; Median time to first reply; Volume by board.
2. Volume & backlog: Created vs closed by day; Open by column; Backlog aging;
   Volume by board.
3. Response time: First reply p50/p90 by week; Cards by tag; Reopened by week
   (only if history exists).
4. Team workload: Closed by assignee; Open assigned by assignee; Emails sent by
   member; Median handle time by assignee.
5. Tags & drivers: Volume by tag; Slowest tags; New vs returning contacts;
   Volume by hour.

Filters: Board, Assignee, Tag, Column/status, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_drag_cards, modeled_drag_boards,
modeled_drag_emails, modeled_drag_users, and modeled_drag_contacts.

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. Keep it practical, dense,
and executive-readable. Avoid vanity metrics.

The basic volume trend over the last 30 days.

SELECT
  date_trunc('day', c.created_at) AS day,
  COUNT(*)                                          AS created,
  COUNT(*) FILTER (WHERE c.is_closed = true)        AS closed
FROM cards c
WHERE c.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1;

Median from the first outbound email per card.

WITH first_outbound AS (
  SELECT
    e.card_id,
    MIN(e.sent_at) AS first_reply_at
  FROM emails e
  WHERE e.direction = 'outbound'
    AND e.is_note = false
  GROUP BY e.card_id
)
SELECT
  date_trunc('week', c.created_at) AS week,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (f.first_reply_at - c.created_at)) / 60.0
  ) AS median_first_reply_min
FROM cards c
JOIN first_outbound f ON f.card_id = c.id
GROUP BY 1
ORDER BY 1;

Where open cards are piling up across shared inboxes.

SELECT
  b.name             AS board,
  COUNT(*)           AS open_cards
FROM cards c
JOIN boards b ON b.id = c.board_id
WHERE c.is_closed = false
GROUP BY b.name
ORDER BY open_cards DESC;

Open and closed cards per team member over 30 days.

SELECT
  u.name             AS assignee,
  COUNT(*) FILTER (WHERE c.is_closed = false) AS open_cards,
  COUNT(*) FILTER (WHERE c.is_closed = true)  AS closed_cards
FROM cards c
JOIN users u ON u.id = c.assignee_id
WHERE c.created_at >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY u.name
ORDER BY open_cards DESC;