How to build Attio sales dashboards in Metabase

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

# 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 Attio CSV export — creates a table AND a model
mb upload csv --file attio-deals.csv --collection root

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

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

Goal: Help sales leaders understand pipeline, win rate, conversion, sales cycle,
and relationship coverage from Attio CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Attio tables and
  models). If durable Attio 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 Attio MCP server (read operations are
  auto-approved; writes request confirmation): records (people, companies), the
  Deals object, lists and list entries, notes, and tasks. 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:
Attio has a flexible, customizable data model, so do not assume exact object or
attribute names. CSV exports are usually flat and pre-aggregated (one row per
deal, company, or person), while warehouse tables are raw and can carry
stage-change history for velocity metrics. Inspect the actual tables and column
names first; do not assume names or that stage history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Attio — it reads a database or CLI-uploaded tables.
- Only recompute rates over the correct base (e.g. win rate) when raw counts are
  available; if the data already provides a rate, chart it directly.
- Define "won" and "closed" once (from the deal stage/status attribute) and reuse
  them.
- For win rate, state the denominator explicitly (won / closed vs. won / created)
  and hold the cohort fixed.
- Report sales cycle length and deal size as medians (p50) and p90, never plain
  averages — both are right-skewed.
- If stage-change history is missing, do not calculate sales cycle length,
  time-in-stage, or stage conversion. Use a caveat instead.
- Convert all amounts to a single reporting currency; caveat any mix.
- Only build a card if its underlying column/metric exists in the data.
- 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: Attio Sales Overview

Sections:
1. Executive summary (KPI cards): Open pipeline; Coverage vs. target; Win rate
   (last 90 days); Median sales cycle length; Average and median deal size; Deals
   closing this period.
2. Pipeline: Open deals by stage; value by owner and list; stale deals.
3. Conversion: Stage-to-stage conversion; win rate by segment and source; loss
   reasons.
4. Velocity: Median cycle length; time-in-stage; deal velocity; aging.
5. Relationships: Companies by segment; new records by week; records with an open
   deal.

Filters: Deal stage, Owner, Segment, Source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_attio_deals, modeled_attio_stage_history,
modeled_attio_companies, modeled_attio_people, and modeled_attio_activity.

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. Reconcile totals against
Attio's own reports. Keep it practical, dense, and executive-readable. Avoid
vanity metrics.

Won as a share of closed deals over the last 12 months.

SELECT
  date_trunc('month', d.closed_at) AS month,
  COUNT(*) FILTER (WHERE d.is_won)                 AS won,
  COUNT(*) FILTER (WHERE d.is_closed)              AS closed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE d.is_won)
      / NULLIF(COUNT(*) FILTER (WHERE d.is_closed), 0),
    1
  ) AS win_rate_pct
FROM modeled_attio_deals d
WHERE d.is_closed
  AND d.closed_at >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

The current funnel — deals and value by stage.

SELECT
  d.stage,
  COUNT(*)               AS open_deals,
  ROUND(SUM(d.value), 2) AS open_value
FROM modeled_attio_deals d
WHERE NOT d.is_closed
GROUP BY d.stage
ORDER BY open_value DESC;

Days from created to won; medians beat averages here.

-- Median days from created to won, by month closed
SELECT
  date_trunc('month', d.closed_at) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closed_at - d.created_at)) / 86400.0
  ) AS median_cycle_days,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (d.closed_at - d.created_at)) / 86400.0
  ) AS p90_cycle_days
FROM modeled_attio_deals d
WHERE d.is_won
GROUP BY 1
ORDER BY 1;