How to build Shippo shipping dashboards in Metabase

Shippo is a multi-carrier shipping platform — rate shopping, label purchase, tracking, and returns across USPS, UPS, FedEx, DHL, and dozens of regional carriers, through one API. Metabase is where you turn that data into shared, trustworthy dashboards. This guide covers two complementary paths: a lightweight MCP + CLI route that pulls live data with the Shippo MCP Server and loads a CSV into Metabase with the Metabase CLI, and a durable pipeline route that lands Shippo data in a database so you can build dashboards anyone can read.

How do you connect Shippo to Metabase?

Most teams combine both routes: quick answers through MCP and CLI uploads first, then recurring reporting on a warehouse-backed model.

What can you analyze from Shippo data in Metabase?

Each of these is built from shipping transactions (labels) joined to the related rates and carrier accounts, parcels and dimensions, tracking status updates data your sync exposes:

• Shipping spend by carrier and service level
• Shipping cost per order and as a share of order value
• Cost by zone and weight band
• On-time delivery and transit time percentiles
• Voided and refunded label recovery

Which Shippo dashboards should you build in Metabase?

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

Pair the Shippo 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 the last 90 days of shipping transactions (labels), one row per label with carrier, service level, billed cost, and zone.
• Export the result as CSV, keeping stable IDs, dates, currency codes, and the fee breakdown intact.
• 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.
• Cost trends need labels across complete months, and carrier adjustments post days after purchase — late-arriving rows will restate recent periods.
• mb upload csv needs an uploads database configured under Admin → Settings → Uploads.

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

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

One of the cleanest vendor MCP stories in shipping: Shippo maintains both the hosted server above and the @shippo/shippo-mcp npm package for stdio-only clients, headless automation with an API key, or data-residency requirements. Shippo also runs a separate documentation server at https://docs.goshippo.com/mcp that searches the API reference — useful alongside the data server, but it returns docs, not your shipments. Note that the npm package's linked GitHub repo is not public; install from npm.

# 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 shipping-transactions-(labels) export — creates a table AND a model
mb upload csv --file shippo-shipping-transactions-(labels).csv --collection root

# Refresh that same table later from a new export
mb upload replace <table-id> --file shippo-shipping-transactions-(labels).csv

Can you generate a Shippo dashboard with AI?

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

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

Goal: Help the team understand label spend, shipping cost per order, carrier and zone cost drivers, and on-time delivery from Shippo data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for shippo tables and
  models). If durable Shippo data is already present — a warehouse sync, a
  direct database connection, or an earlier upload — use it and skip to Step 2.
- If nothing is there, pull a scoped, summarized export with the Shippo MCP Server:
  shipping transactions (labels), plus rates and carrier accounts, parcels and dimensions, tracking status updates.
  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, products,
locations or countries, currencies, and date ranges before creating trend cards.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Shippo — it reads a database or CLI-uploaded tables.
- Separate gross amounts, platform and processing fees, taxes, and refunds into
  distinct columns; never let one card silently mix gross and net.
- 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.
- Cost trends need labels across complete months, and carrier adjustments post days after purchase — late-arriving rows will restate recent periods.
- If a metric can't be computed from the data present, say so on the card
  instead of approximating it.

Dashboard title: Shippo Shipping & Delivery Overview

Sections:
1. Executive summary: Label spend this month; Cost per order;
   Cost as % of order value; On-time delivery rate; Tracking coverage.
2. Spend: Spend by month; by carrier and service level; voided labels.
3. Cost drivers: Cost by zone and weight band; carrier adjustments.
4. Delivery: On-time rate; transit p50/p90 by carrier; late by zone.
5. Strategy: Cost per order by carrier; service mix; revenue vs. cost.

Filters: Date range, Carrier, Service level, Zone, Weight band.

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 Shippo data into a database or warehouse?

For dashboards that need history and reliability, land Shippo data in a database first, then connect Metabase to that database.

Connector options

• Managed connector — use Airbyte, Fivetran, or dlt when one genuinely covers the objects you need.
• Custom pipeline — use the Shippo Transactions API for control over grain, fields, and refresh cadence.
• MCP + CSV — use this for quick exploration and one-off slices.

Read this one carefully: the Airbyte Shippo source syncs addresses, parcels, shipments, carrier accounts, and custom items — but <strong>not transactions</strong>, which is where label costs live. For cost reporting you need a scheduled pull against the Transactions API instead. Two API limits shape that job: list endpoints are capped at 50 requests per minute on live keys, and rates and shipments older than 390 days are no longer returned — so backfill early and page politely.

Notes

• Decide the grain first (one row per label, aggregated to order level for cost per order) — it drives every trend card.
• Land raw tables first, then build clean Metabase models on top.
• Normalize label-date, order-id, carrier, service-level, billed-cost, zone, weight, and void-status fields.

How should you model Shippo data in Metabase?

Core tables

Modeling advice

• Build a clean shipping_labels model with the same column names you use for sibling tools, so cross-tool dashboards don't fork definitions.
• Keep gross amount, platform fee, processing fee, tax, and net amount as separate, labeled columns — never let one card silently mix them.
• Snapshot the carrier's estimated delivery date at label purchase into eta_at_purchase — carriers revise ETAs after the fact, and an on-time rate measured against a moving promise flatters itself.
• Exclude labels with a REFUNDED or REFUNDPENDING status from cost totals, but keep them in the table so you can report recovered spend.
• Aggregate multi-package orders to order grain before averaging, or cost per order silently becomes cost per package.
• Store amounts in minor units or with an explicit currency column, and convert once in the model layer rather than per card.

Which Shippo metrics should you track in Metabase?

What SQL powers Shippo dashboards in Metabase?

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

WITH per_order AS (
  -- Roll labels up to the order before averaging, and drop refunds.
  SELECT
    l.order_id,
    date_trunc('month', MIN(l.object_created))::date AS month,
    SUM(l.amount) AS label_cost,
    COUNT(*) AS packages
  FROM shipping_labels l
  WHERE l.status = 'SUCCESS'
  GROUP BY l.order_id
)
SELECT
  p.month,
  COUNT(*) AS orders_shipped,
  ROUND(AVG(p.label_cost), 2) AS avg_cost_per_order,
  ROUND(
    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY p.label_cost)::numeric, 2
  ) AS median_cost_per_order,
  ROUND(
    100.0 * SUM(p.label_cost) / NULLIF(SUM(o.order_total), 0), 1
  ) AS cost_pct_of_order_value
FROM per_order p
JOIN orders o ON o.order_id = p.order_id
GROUP BY p.month
ORDER BY p.month;

SELECT
  carrier,
  service_level,
  zone,
  COUNT(*) AS labels,
  ROUND(AVG(amount), 2) AS avg_label_cost,
  ROUND(SUM(amount), 2) AS total_spend,
  ROUND(AVG(parcel_weight), 2) AS avg_weight_lb
FROM shipping_labels
WHERE status = 'SUCCESS'
  AND object_created >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY carrier, service_level, zone
HAVING COUNT(*) >= 25
ORDER BY total_spend DESC;

SELECT
  date_trunc('month', shipped_at)::date AS month,
  carrier,
  COUNT(*) AS shipments,
  -- Coverage: only shipments with a delivery scan can be judged at all.
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE delivered_at IS NOT NULL)
    / NULLIF(COUNT(*), 0), 1
  ) AS tracking_coverage_pct,
  ROUND(
    100.0 * COUNT(*) FILTER (
      WHERE delivered_at IS NOT NULL
        AND delivered_at::date <= eta_at_purchase::date
    ) / NULLIF(COUNT(*) FILTER (WHERE delivered_at IS NOT NULL), 0), 1
  ) AS on_time_pct,
  ROUND(
    PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY days_in_transit)::numeric, 1
  ) AS p90_transit_days
FROM shipment_tracking
WHERE shipped_at >= CURRENT_DATE - INTERVAL '180 days'
GROUP BY 1, 2
ORDER BY 1 DESC, shipments DESC;

What are common mistakes when analyzing Shippo in Metabase?

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