How to build Salesforce sales dashboards in Metabase

{
  "mcpServers": {
    "salesforce": {
      "url": "https://api.salesforce.com/platform/mcp/v1/platform/sobject-reads",
      "auth": {
        "CLIENT_ID": "YOUR_CONSUMER_KEY"
      }
    }
  }
}

# Sandbox / scratch orgs use the /sandbox/ path segment
https://api.salesforce.com/platform/mcp/v1/sandbox/platform/sobject-reads

# 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 Salesforce CSV export — creates a table AND a model
mb upload csv --file salesforce-opportunities.csv --collection root

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

Create a polished Metabase dashboard for Salesforce 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, forecast, win rate, sales cycle,
and rep activity from Salesforce CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Salesforce tables and
  models). If durable Salesforce 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 Salesforce hosted MCP server (read-only
  where supported): opportunities, opportunity stages, accounts, activities, and
  users. 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:
Salesforce CSV exports are usually flat and pre-aggregated (one row per
opportunity or account, with columns like stage, amount, close date, and owner).
Warehouse tables are raw and include OpportunityFieldHistory for velocity
metrics. Inspect the actual tables and column names first; do not assume exact
names or that stage history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Salesforce — 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 (IsWon vs. IsClosed) and reuse the definitions.
- 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 OpportunityFieldHistory (stage 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: Salesforce Sales Overview

Sections:
1. Executive summary (KPI cards): Open pipeline; Pipeline coverage vs. quota;
   Win rate (last 90 days); Median sales cycle length; Average and median deal
   size; Opportunities closing this period.
2. Pipeline: Open pipeline by stage; by owner and segment; aging and past-due
   close dates.
3. Forecast: Weighted vs. unweighted pipeline; forecast category; opportunities
   expected to close by week; slipped opportunities.
4. Win rate & conversion: Win rate by month and segment; stage-to-stage
   conversion; loss reasons.
5. Activity: Tasks/events by rep; activities per open opportunity; new-lead
   response time.

Filters: Stage, Owner, Segment/Industry, Lead source, Forecast category, Date
range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_sfdc_opportunities, modeled_sfdc_accounts,
modeled_sfdc_stage_history, modeled_sfdc_activities, and modeled_sfdc_users.

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

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

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

The current funnel plus a probability-weighted forecast.

SELECT
  o.stage_name,
  o.forecast_category,
  COUNT(*)                 AS open_opps,
  ROUND(SUM(o.amount), 2)  AS open_amount,
  ROUND(SUM(o.amount * o.probability / 100.0), 2) AS weighted_amount
FROM modeled_sfdc_opportunities o
WHERE NOT o.is_closed
GROUP BY o.stage_name, o.forecast_category
ORDER BY open_amount DESC;

Days from create to close-won; medians beat averages here.

-- Median days from create to close-won, by month closed
SELECT
  date_trunc('month', o.close_date) AS month,
  percentile_cont(0.5) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (o.closed_at - o.created_date)) / 86400.0
  ) AS median_cycle_days,
  percentile_cont(0.9) WITHIN GROUP (
    ORDER BY EXTRACT(EPOCH FROM (o.closed_at - o.created_date)) / 86400.0
  ) AS p90_cycle_days
FROM modeled_sfdc_opportunities o
WHERE o.is_won
GROUP BY 1
ORDER BY 1;