Stocking Guide — Auction Buying Intelligence for Independent Dealers

User Context

The primary user is an independent dealer (owner, buyer, or inventory manager) who attends 2-3 auctions per week and needs to make bid/no-bid decisions in minutes. They are buying 15-40 vehicles per month at auction and every bad buy ties up floor plan capital for 60-90+ days. The difference between data-driven and gut-instinct buying is $2,000-$3,000 per unit in avoided losses on slow movers.

The following fields are loaded from the dealer profile automatically:

Gotchas

• Hot List and Avoid List workflows are US-only — they require get_sold_summary which is not available for UK. Pre-Auction VIN Check and Category Gap Finder work for UK with comp-based pricing.
• Facet query syntax: field|offset|limit|min_count — e.g., body_type|0|20|1 means "facet on body_type, starting at offset 0, return up to 20 buckets, minimum 1 document per bucket."
• Demand-to-Supply Ratio = monthly sold / active supply — NOT the reverse. A ratio > 2.5 is strong demand, 1.5-2.5 moderate, < 1.5 oversupplied.
• CPO max bid has an extra deduction — CPO Max Bid = CPO predicted_retail × (1 - margin%) - recon_cost - cpo_certification_cost. The cert cost is separate from recon.
• ~1.5% monthly depreciation assumption for holding cost estimates is a rough industry average — actual rates vary significantly by vehicle age, segment, and brand. Use sold data when available instead.
• market-demand-agent output format — the agent returns structured results; pass it the state, dealer_type, zip, radius, and date range. It handles get_sold_summary calls internally.

Workflow: Pre-Auction VIN Check

Use this when a dealer says "check these VINs from tomorrow's auction" or "should I bid on this one." This is the most time-critical workflow — the dealer may be standing at the auction lane with 60 seconds to decide.

1. Decode the VIN(s) — For each VIN provided, call mcp__marketcheck__decode_vin_neovin with vin. Extract: year, make, model, trim, body_type, drivetrain, engine, fuel_type, transmission. Present a one-line summary per vehicle so the dealer can confirm they are looking at the right unit.

2. Get predicted retail values (dual) — For each VIN, make TWO calls to mcp__marketcheck__predict_price_with_comparables:

   • Primary: with vin, miles, zip, dealer_type matching the dealer's type from profile. This is the price the dealer can expect to retail at.
   • Secondary: with the OTHER dealer_type. This provides cross-market context. Record both predicted prices.

2a. CPO pricing (if dealer has CPO program) — If dealer.cpo_program=true in profile and the vehicle is eligible for certification (used, recent model year, reasonable mileage):

• Call predict_price_with_comparables with is_certified=true plus the dealer's dealer_type to get the CPO retail value.
• Calculate CPO Max Bid = CPO predicted_retail × (1 - target_margin%) - recon_cost - cpo_certification_cost
• Show both scenarios:

IF CERTIFIED:
  CPO Retail Value: $XX,XXX | CPO Max Bid: $XX,XXX (includes $X,XXX cert cost)
IF SOLD AS-IS:
  Standard Retail:  $XX,XXX | Standard Max Bid: $XX,XXX

3. Check local supply — For each VIN, call mcp__marketcheck__search_active_cars with year, make, model, trim (from decode), zip (dealer's zip), radius (dealer's radius, default 75), car_type=used, stats=price,dom, rows=5, sort_by=dom, sort_order=asc. Record: total matching listings (supply count), median price, average DOM of active competing units.

4. Check recent sold velocity — Call mcp__marketcheck__get_sold_summary with make, model, state (dealer's state), inventory_type=Used, date_from (first of prior month), date_to (last of prior month). Record: sold_count and average_days_on_market.

5. Calculate the verdict — For each VIN, compute:

   • Max Bid = PRIMARY predicted_retail_price × (1 - target_margin%) - recon_cost
   • Demand-to-Supply Ratio = monthly sold_count / active supply count. Above 3.0 = strong demand, 1.5-3.0 = moderate, below 1.5 = oversupplied.
   • Expected Turn Days = average_days_on_market from sold data
   • Estimated Floor Plan Cost = expected_turn_days x daily_floor_plan_cost
   • Projected Net Profit = predicted_retail_price - max_bid - recon_cost - floor_plan_cost

   Assign a verdict:

   • BUY — Demand-to-supply ratio > 2.5, expected turn < 35 days, projected profit > $2,000
   • CAUTION — Demand-to-supply ratio 1.5-2.5, expected turn 35-50 days, projected profit $1,000-$2,000
   • PASS — Demand-to-supply ratio < 1.5, expected turn > 50 days, or projected profit < $1,000

   Show both market values:

   Franchise Retail Value:    $XX,XXX
   Independent Retail Value:  $XX,XXX
   Your Retail (based on [type]): $XX,XXX ← used for Max Bid
   Max Bid:                   $XX,XXX
   

Workflow: Hot List Generator

Use this when a dealer asks "what's selling fast in my area" or "what should I actively look for at auction this week."

Multi-agent approach: Use the market-demand-agent to generate the hot list with all demand analytics in a single agent call.

Use the Agent tool to spawn the marketcheck-cowork-plugin:market-demand-agent agent with this prompt:

Generate stocking hot list for state=[state], dealer_type=[dealer_type], zip=[zip], radius=[radius], target_margin_pct=[target_margin], recon_cost=[recon_cost]. Date range: [first day of prior month] to [last day of prior month]. Sections: hot_list.

The agent will:

1. Get fastest-turning models via get_sold_summary (by average_days_on_market)
2. Get highest-volume sellers via get_sold_summary (by sold_count)
3. Check supply for cross-referenced models via search_active_cars (rows=0)
4. Calculate D/S ratios, opportunity scores, and max auction buy prices
5. Return a ranked top 10 hot list

Cross-reference with current lot: If the dealer's lot data is available (from a prior lot-scanner run or dealer_id), check which hot-list models the dealer already has. Flag gaps.

Present the top 10 as: Rank, Make/Model, Avg Days to Sell, Monthly Sold Volume, Active Supply, D/S Ratio, Franchise Median, Independent Median, Max Auction Buy Price ([dealer_type] basis), On Your Lot?

Workflow: Category Gap Finder

Use this when a dealer asks "is my inventory mix right" or "what categories am I missing."

1. Get dealer's current inventory mix — Call mcp__marketcheck__search_active_cars with dealer_id (dealer's ID), facets=body_type|0|20|1,make|0|30|1,fuel_type|0|10|1, rows=0. This returns the dealer's current inventory breakdown by body type, make, and fuel type without returning individual listings.

2. Get market demand by category — Call mcp__marketcheck__get_sold_summary with state (dealer's state), inventory_type=Used, ranking_dimensions=body_type, ranking_measure=sold_count, ranking_order=desc, date_from (first of prior month), date_to (last of prior month), top_n=15.

3. Get market demand by make — Call mcp__marketcheck__get_sold_summary with state (dealer's state), inventory_type=Used, ranking_dimensions=make, ranking_measure=sold_count, ranking_order=desc, date_from (first of prior month), date_to (last of prior month), top_n=25.

4. Calculate alignment score — For each body type and make:

   • Dealer share = dealer's count in that category / dealer's total inventory x 100
   • Market share = market sold_count in that category / total market sold_count x 100
   • Gap = Market share - Dealer share (positive = dealer is under-indexed, negative = over-indexed)
   • Flag gaps > 5 percentage points as significant mismatches

5. Deliver the gap analysis — Present two tables:

   • Under-stocked categories (market share > dealer share by 5%+): these are categories the dealer should buy more of at auction. Include the market turn rate and average price for context.
   • Over-stocked categories (dealer share > market share by 5%+): these are categories where the dealer has excess exposure. Recommend slowing acquisition and letting natural sales reduce the count. Include a one-line mix recommendation (e.g., "Your lot is 40% sedans but your market is 55% SUVs — shift 3-4 units per month from sedans to SUVs").

Workflow: Avoid List (Slow Movers)

Use this when a dealer asks "what should I stay away from" or "which vehicles are sitting." This prevents the most costly mistake: buying a vehicle that sits for 90+ days eating floor plan.

1. Get slowest-turning models — Call mcp__marketcheck__get_sold_summary with state (dealer's state), inventory_type=Used, ranking_dimensions=make,model, ranking_measure=average_days_on_market, ranking_order=desc, top_n=20, date_from (first of prior month), date_to (last of prior month). These are models with the longest average time to sell.

2. Get supply context — For each of the top 20 slow movers, call mcp__marketcheck__search_active_cars with make, model, state (use seller_state or zip+radius), car_type=used, stats=price,dom, rows=0. Record: active supply count and average DOM of current unsold inventory.

3. Get sold volume — Call mcp__marketcheck__get_sold_summary with state (dealer's state), inventory_type=Used, ranking_dimensions=make,model, ranking_measure=sold_count, ranking_order=asc, top_n=20, date_from (first of prior month), date_to (last of prior month). Cross-reference with step 1 — models that are both slow AND low-volume are the most dangerous.

4. Calculate holding cost exposure — For each slow mover:

   • Estimated holding period = average_days_on_market from sold data
   • Floor plan cost = holding period x daily_floor_plan_cost
   • Depreciation during hold = (holding period / 30) x 1.5% x average_sale_price (assumes ~1.5% monthly depreciation while sitting)
   • Total cost of slow turn = floor_plan_cost + depreciation_during_hold
   • Oversupply ratio = active supply count / monthly sold_count (above 3.0 = dangerously oversupplied)

5. Deliver the Avoid List — Present a table: Make/Model, Avg Days to Sell, Monthly Sold Volume, Active Supply, Oversupply Ratio, Est. Floor Plan Cost, Est. Depreciation Loss, Total Holding Cost. Add a header warning: "Buying any of these models costs an estimated $X,XXX-$X,XXX in holding costs before you sell it. This directly reduces — or eliminates — your front-end gross."

KPIs & Business Impact

→ After assembling results, read references/outcomes.md to frame recommendations with quantified business impact, KPI benchmarks, and action-to-outcome guidance.

Output Format

Always present results in this structure, optimized for quick reading on a phone at the auction:

For Pre-Auction VIN Checks (single VIN — keep it fast):

VERDICT: BUY / CAUTION / PASS

Vehicle: 2022 Toyota RAV4 XLE AWD — 34,200 mi
VIN: XXXXXXXXXXXXXXXXX

Max Bid:        $24,800
Retail Value:   $30,500
Expected Turn:  28 days
Demand/Supply:  3.8 (strong)
Projected Net:  $2,950

Supply: 14 competing units within 75 mi
        Median asking: $31,200
        Fastest comp sold in 18 days

For Pre-Auction VIN Checks (batch — table format):

For Hot List / Avoid List:

Present as a numbered list with the most important models first. Include the Max Auction Buy Price for hot list items and the Estimated Holding Cost for avoid list items. Keep it to 10 items max — the dealer needs to remember these while walking the auction lanes.

For Category Gap Analysis:

Two short lists: "Buy More Of" and "Slow Down On" with the market vs dealer share numbers and a concrete unit count recommendation (e.g., "Add 3 more SUVs, reduce sedans by 2").

Self-Check (before presenting to user)

• Every VIN has a clear verdict: BUY / CAUTION / PASS
• Max Bid calculated using profile's margin% and recon cost (not hardcoded)
• D/S ratios show correct direction (sold/supply, NOT supply/sold)
• Both franchise and independent retail values shown per VIN
• Hot list limited to 10 items (dealer needs to remember at auction)
• Floor plan cost uses profile's cost/day value