How to build CentralStationCRM sales dashboards in Metabase

{
  "mcpServers": {
    "centralstationcrm": {
      "url": "https://mcp.pipedream.net/v2"
    }
  }
}

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

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

Create a polished Metabase dashboard for CentralStationCRM 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 activity from CentralStationCRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for CentralStationCRM
  tables and models). If durable CentralStationCRM 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 CentralStationCRM MCP server (managed via
  Pipedream): deals, people, companies, to-dos, and protocols. 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:
CentralStationCRM CSV exports are usually flat and pre-aggregated (one row per
deal or to-do, with columns like status, volume, and dates). Warehouse tables are
raw and relational (deals, to-dos, and status history join on IDs). Inspect the
actual tables and column names first; do not assume exact names or that
status-change history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  CentralStationCRM — it reads a database or CLI-uploaded tables.
- Define "won" and "closed" once (deal status) 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 value as medians (p50) and p90, never plain
  averages — both are right-skewed.
- Only build a card if its underlying column/metric exists in the data; if deal
  status-change history is missing, skip sales cycle length, time-in-status, and
  status conversion and use a caveat instead.
- Convert all amounts to a single reporting currency; caveat any mix.
- 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: CentralStationCRM 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 value;
   Open to-dos.
2. Pipeline: Open deals by status; value by owner; deals with overdue to-dos.
3. Conversion: Status-to-status conversion; win rate by owner and source; lost
   reasons.
4. Activity: To-dos completed by owner; open to-dos per deal; overdue to-dos.
5. Velocity: Median cycle length; time-in-status; deal velocity; aging.

Filters: Status, Owner, Source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_csc_deals, modeled_csc_status_history,
modeled_csc_people, modeled_csc_companies, and modeled_csc_todos.

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
CentralStationCRM's own reporting. Keep it practical, dense, and
executive-readable.

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.status = 'won')                   AS won,
  COUNT(*) FILTER (WHERE d.status IN ('won', 'lost'))        AS closed,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE d.status = 'won')
      / NULLIF(COUNT(*) FILTER (WHERE d.status IN ('won', 'lost')), 0),
    1
  ) AS win_rate_pct
FROM modeled_csc_deals d
WHERE d.status IN ('won', 'lost')
  AND d.closed_at >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

The current funnel — open deals and value by status.

SELECT
  d.status,
  COUNT(*)                AS open_deals,
  ROUND(SUM(d.volume), 2) AS pipeline_value
FROM modeled_csc_deals d
WHERE d.status = 'open'
GROUP BY d.status
ORDER BY pipeline_value DESC;

Where follow-through has slipped, by owner.

-- Open deals with overdue to-dos
SELECT
  d.id,
  d.name,
  d.owner,
  COUNT(*) FILTER (WHERE t.completed_at IS NULL AND t.due_date < CURRENT_DATE) AS overdue_todos
FROM modeled_csc_deals d
JOIN modeled_csc_todos t ON t.deal_id = d.id
WHERE d.status = 'open'
GROUP BY d.id, d.name, d.owner
HAVING COUNT(*) FILTER (WHERE t.completed_at IS NULL AND t.due_date < CURRENT_DATE) > 0
ORDER BY overdue_todos DESC;