How to build Breakcold sales dashboards in Metabase

{
  "mcpServers": {
    "breakcold": {
      "url": "https://http.us.breakcold.com/mcp/v1"
    }
  }
}

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

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

Create a polished Metabase dashboard for Breakcold 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 social outreach from Breakcold CRM data.

Step 1 — Find or load the data:
- First, check what already exists in Metabase (search for Breakcold tables and
  models). If durable Breakcold 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 Breakcold MCP server: contacts,
  companies, deals, tasks, notes, and activities across channels. 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:
Breakcold CSV exports are usually flat and pre-aggregated (one row per deal or
activity, with columns like stage, status, amount, channel, and direction).
Warehouse tables are raw and relational (deals, activities, and stage history
join on IDs). Inspect the actual tables and column names first; do not assume
exact names or that stage-change history exists.

Important:
- Build on whatever data is present; don't claim Metabase connects natively to
  Breakcold — 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 size 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
  stage-change history is missing, skip sales cycle length, time-in-stage, and
  stage conversion and use a caveat instead.
- Convert all amounts to a single reporting currency; caveat any mix.
- Attribute activities to their channel so outreach analysis stays honest.
- 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: Breakcold 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;
   Reply rate.
2. Pipeline: Open deals by stage; value by owner and pipeline; stale deals.
3. Conversion: Stage-to-stage conversion; win rate by pipeline and source; lost
   reasons.
4. Outreach: Activities by channel; touches per deal; reply rate by channel.
5. Velocity: Median cycle length; time-in-stage; deal velocity; aging.

Filters: Pipeline, Stage, Owner, Channel, Source, Date range.

Reuse the models Metabase auto-created from uploaded CSVs, or (for a warehouse)
create reusable models: modeled_breakcold_deals,
modeled_breakcold_stage_history, modeled_breakcold_contacts,
modeled_breakcold_companies, and modeled_breakcold_activities.

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
Breakcold'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_breakcold_deals d
WHERE d.status IN ('won', 'lost')
  AND d.closed_at >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1
ORDER BY 1;

Inbound replies as a share of outbound touches, by channel.

-- Reply rate by outreach channel over the last 90 days
SELECT
  a.channel,
  COUNT(*) FILTER (WHERE a.direction = 'outbound')              AS sent,
  COUNT(*) FILTER (WHERE a.direction = 'inbound')               AS replies,
  ROUND(
    100.0 * COUNT(*) FILTER (WHERE a.direction = 'inbound')
      / NULLIF(COUNT(*) FILTER (WHERE a.direction = 'outbound'), 0),
    1
  ) AS reply_rate_pct
FROM modeled_breakcold_activities a
WHERE a.occurred_at >= CURRENT_DATE - INTERVAL '90 days'
GROUP BY a.channel
ORDER BY sent DESC;

The current funnel — open deals and value by stage.

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