How to build eBay dashboards in Metabase

{
  "mcpServers": {
    "ebay": {
      "command": "npx",
      "args": [
        "-y",
        "mcp-remote",
        "https://mcp.apideck.com/ebay"
      ]
    }
  }
}

# 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 an eBay CSV export — creates a table AND a model
mb upload csv --file ebay-orders.csv --collection root

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

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

Goal: Help eBay sellers understand sales, average order value, listing performance,
fulfillment, and fees/profitability from eBay Sell API data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for eBay tables and
  models). If durable eBay 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 eBay MCP server in read-only mode:
  orders, line items, listings/inventory, fulfillments/shipments, and
  fees/payouts. 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:
eBay CSV exports are often flat (one row per order or listing, with columns like
total amount, quantity, fees, and fulfillment status). Warehouse tables are raw
and order-grained (orders, line items, listings, fees). Inspect the actual tables
and column names first; do not assume exact names. Map the available raw tables
into these analytical concepts where possible: Orders, Line items,
Listings/inventory items, Categories, Fulfillments/shipments, and Fees/payouts.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  eBay — it reads a database or CLI-uploaded tables.
- Report NET proceeds: subtract eBay final value and store fees, and refunds.
- Use order-level grain for AOV.
- Only build fulfillment SLA cards if shipment/handling data is present.
- Separate marketplaces (US, UK, etc.) and currencies.
- 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: eBay Seller Overview

Sections:
1. Executive summary (KPI cards): Net proceeds last 30 days; Orders; AOV; Fees as %
   of sales; On-time shipment rate.
2. Sales & demand: Net proceeds by day; Orders by day; AOV trend; Revenue by category.
3. Listing performance: Top listings by net proceeds; Units by listing; Sell-through.
4. Fulfillment: Orders awaiting shipment; On-time rate; Handling time.
5. Fees & profitability: Fees % of sales; Fee breakdown; Net margin by category.

Filters: Date range, Marketplace, Category, Listing, Fulfillment status.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_ebay_orders, modeled_ebay_line_items,
modeled_ebay_listings, and modeled_ebay_fees.

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.

Paid orders over the last 30 days.

SELECT
  date_trunc('day', o.creation_date)                  AS day,
  COUNT(*)                                            AS orders,
  SUM(o.total_amount)                                 AS gross_sales
FROM orders o
WHERE o.order_payment_status = 'PAID'
  AND o.creation_date >= CURRENT_DATE - INTERVAL '30 days'
GROUP BY 1
ORDER BY 1;

AOV at the order grain, on paid orders.

SELECT
  date_trunc('week', o.creation_date) AS week,
  COUNT(*)                            AS orders,
  ROUND(SUM(o.total_amount) / NULLIF(COUNT(*), 0), 2) AS aov
FROM orders o
WHERE o.order_payment_status = 'PAID'
GROUP BY 1
ORDER BY 1;

Units and gross revenue by listing over 90 days.

SELECT
  li.title                         AS listing,
  SUM(li.quantity)                 AS units,
  SUM(li.line_item_cost)           AS gross_revenue
FROM line_items li
JOIN orders o ON o.order_id = li.order_id
WHERE o.order_payment_status = 'PAID'
  AND o.creation_date >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY li.title
ORDER BY gross_revenue DESC
LIMIT 20;

Fulfillment SLA performance over time.

SELECT
  date_trunc('week', o.creation_date) AS week,
  ROUND(100.0 * COUNT(*) FILTER (WHERE o.shipped_on_time = true)
    / NULLIF(COUNT(*) FILTER (WHERE o.fulfillment_status = 'FULFILLED'), 0), 2)
    AS on_time_rate_pct
FROM orders o
GROUP BY 1
ORDER BY 1;