dealer-group-health-monitor

name	dealer-group-health-monitor
description	Public dealer group health and investment signals. Triggers: "dealer group stock", "how is AutoNation doing", "LAD health check", "publicly traded dealer analysis", "dealer group efficiency", "CarMax performance", "Carvana metrics", "dealer group benchmarking", "retail auto stock signal", "dealer group volume", operational health monitoring for publicly traded dealer groups and automotive retailers.
version	0.1.0

Date anchor: Today's date comes from the # currentDate system context. Compute ALL relative dates from it. Example: if today = 2026-03-14, then "prior month" = 2026-02-01 to 2026-02-28, "current month" (most recent complete) = February 2026, "three months ago" = December 2025. Never use training-data dates.

get_sold_summary parameter safety:

• Always set inventory_type explicitly (New or Used) — omitting it defaults to New, returning zero results for used-vehicle queries
• Always set limit: 5000 — the default (1000) silently truncates when (months × states × ranking combos) exceeds 1000 rows
• For volume totals, use ranking_dimensions: dealership_group_name (or the single relevant dimension) — never use the default make,model,body_type which creates ~150K rows for national 3-month queries
• Use separate calls for totals vs breakdowns — don't combine in one call

Dealer Group Health Monitor — Investment Signals for Publicly Traded Dealer Stocks

User Profile (Load First)

Load the marketcheck-profile.md project memory file. If missing, ask for dealer group or ticker. Extract: group_name, is_publicly_traded, ticker, country. US-only (UK → stop). Confirm profile.

User Context

Dealer group executive or equity analyst benchmarking publicly traded dealer groups (AN, LAD, PAG, SAH, GPI, ABG, KMX, CVNA) on operational health and investment signals.

Built-in Ticker → Dealer Group Mapping

AN    → AutoNation
LAD   → Lithia Motors
PAG   → Penske Automotive Group
SAH   → Sonic Automotive
GPI   → Group 1 Automotive
ABG   → Asbury Automotive Group
KMX   → CarMax
CVNA  → Carvana

Workflow: Single Dealer Group Health Check

Use when a user asks "How is AutoNation doing?" or "LAD health check."

Step 1 — Resolve the entity

Map ticker or name to the dealer group. Confirm: "Analyzing [Ticker] ([Group Name])"

Step 2 — Volume and efficiency (current month)

Call mcp__marketcheck__get_sold_summary with:

• ranking_dimensions: dealership_group_name
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 20
• inventory_type: Used (or New if analyzing new vehicle operations)
• limit: 5000
• date_from / date_to: current month

Also make a dedicated call for the target group's volume:

• dealership_group_name: the target group name
• ranking_dimensions: dealership_group_name
• ranking_measure: sold_count
• inventory_type: Used (or New)
• limit: 5000
• date_from / date_to: current month

→ Extract only: target group's sold_count, average_sale_price, average_days_on_market. Discard full response.

Step 3 — Prior month comparison

Repeat Step 2 for prior month (same parameters including inventory_type and limit: 5000). → Extract only: same fields as Step 2 for prior month. Discard full response. Calculate:

• Volume MoM % = (current - prior) / prior x 100
• ASP MoM % = price change
• DOM MoM change = days change
• Efficiency Score = sold_count / average_days_on_market (higher = better capital efficiency)

Step 4 — Active inventory health

Call mcp__marketcheck__search_active_cars with:

• dealer_group: the group name
• car_type: used
• stats: price,dom
• rows: 0

→ Extract only: total count, avg price (from stats), avg DOM (from stats). Discard full response. Repeat with car_type=new.

Calculate:

• Days Supply (used) = active used inventory / monthly used sold x 30
• Days Supply (new) = active new inventory / monthly new sold x 30
• Inventory Build/Draw: Compare current active count to prior month's — is inventory building or drawing down?

Step 5 — Peer comparison

From the Step 2 results (which already include top 20 dealer groups), extract the top 8 publicly traded groups. Build a peer table with: volume, ASP, DOM, efficiency score.

Rank the target group against peers on each metric.

Step 6 — Segment mix (optional, for deeper analysis)

Call mcp__marketcheck__get_sold_summary with:

• dealership_group_name: the group
• ranking_dimensions: body_type
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 10
• inventory_type: Used (or New)
• limit: 5000

And separately:

• ranking_dimensions: make
• ranking_measure: sold_count
• top_n: 15
• inventory_type: Used (or New)
• limit: 5000

→ Extract only: per body_type/make — sold_count, average_sale_price, average_days_on_market. Discard full response.

Output

DEALER GROUP HEALTH — [Group Name] ([Ticker])
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Period: [Current Month] vs [Prior Month]

OPERATIONAL KPIs
Metric                  | Current    | Prior Mo   | MoM Change | Signal
------------------------|------------|------------|------------|--------
Volume (units sold)     | XX,XXX     | XX,XXX     | +X.X%      | BULLISH/BEARISH
Avg Sale Price          | $XX,XXX    | $XX,XXX    | +X.X%      | signal
Avg Days on Market      | XX days    | XX days    | +X days    | signal
Efficiency Score        | XXX        | XXX        | +X.X%      | signal
(vol / DOM)             |            |            |            |

INVENTORY HEALTH
                    | Active Count | Days Supply | Trend       | Signal
--------------------|-------------|-------------|-------------|--------
Used Inventory      | XX,XXX      | XX days     | Building/Drawing | signal
New Inventory       | XX,XXX      | XX days     | Building/Drawing | signal

PEER COMPARISON (Top 8 Public Dealer Groups)
Rank | Group            | Ticker | Volume  | ASP     | DOM  | Efficiency | Signal
-----|------------------|--------|---------|---------|------|------------|--------
  1  | [Group]          | XX     | XX,XXX  | $XX,XXX | XX   | XXX        | —
  2  | [Group]          | XX     | XX,XXX  | $XX,XXX | XX   | XXX        | —
  ...
★ = [Target Group]

[If segment data available:]
TOP SEGMENTS (by volume)
Segment   | Volume  | % of Total | ASP       | DOM
----------|---------|------------|-----------|------
SUV       | XX,XXX  | XX%        | $XX,XXX   | XX
Pickup    | XX,XXX  | XX%        | $XX,XXX   | XX
Sedan     | XX,XXX  | XX%        | $XX,XXX   | XX

TOP BRANDS SOLD
Make      | Volume  | % of Total | ASP       | DOM
----------|---------|------------|-----------|------
Toyota    | XX,XXX  | XX%        | $XX,XXX   | XX
Ford      | XX,XXX  | XX%        | $XX,XXX   | XX

INVESTMENT THESIS SIGNAL: [BULLISH / BEARISH / MIXED / NEUTRAL]

Positive:
- [e.g., "Volume up 4.2% MoM outpacing industry growth of 1.8%"]
- [e.g., "DOM improvement of 3 days signals better inventory management"]

Negative:
- [e.g., "Days supply building to 52 — may require price reductions"]

Watchpoints:
- [e.g., "Used car ASP declining while volume rises — margin compression risk"]

Signal Logic

Metric	BULLISH	NEUTRAL	CAUTION	BEARISH
Volume MoM	> +3%	-1% to +3%	-3% to -1%	< -3%
ASP MoM	> +1%	-1% to +1%	-3% to -1%	< -3%
DOM Change	< -2 days	-2 to +2	+2 to +5	> +5 days
Days Supply (used)	< 35	35-55	55-75	> 75
Days Supply (new)	< 50	50-80	80-100	> 100
Efficiency MoM	> +5%	-2% to +5%	-5% to -2%	< -5%

Workflow: Peer Group Comparison

Use when the user asks "compare AutoNation vs Lithia" or "rank the top dealer groups."

1. Pull all 8 publicly traded groups from get_sold_summary rankings (with inventory_type set explicitly and limit: 5000)
2. Rank on: Volume, ASP, DOM, Efficiency Score
3. Calculate a composite rank (average of individual ranks)
4. Present side-by-side comparison table
5. Identify which group is gaining/losing relative position

Important Notes

• US-only: All data from get_sold_summary requires US market.
• The dealership_group_name field in MarketCheck may not exactly match the stock ticker name — use fuzzy matching if needed.
• Volume from MarketCheck represents listings activity, not necessarily closed transactions. Use as a proxy for retail velocity.
• DOM is a leading indicator of margin pressure — rising DOM precedes price cuts which precede margin compression in quarterly earnings.
• Efficiency Score (volume / DOM) is the single best proxy for operational health — it captures both demand (volume) and execution (speed).