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.

Stocking Guide — Auction Buying Intelligence for Dealers

Profile

Load the marketcheck-profile.md project memory file if exists. Extract: zip/postcode, state/region, dealer_id, dealer_type, country, radius, target_margin, recon_cost, floor_plan_per_day, max_dom, aging_threshold, cpo_program, cpo_certification_cost. Also extract: default_inventory_type from preferences ("used" | "new" | "both"; default "used" if not set). Apply as car_type in all supply/comp searches. Override if user explicitly states otherwise. Never mix new and used in the same report section. If missing, ask minimum fields. US: decode_vin_neovin, predict_price_with_comparables, search_active_cars, get_sold_summary. UK: search_uk_active_cars, search_uk_recent_cars only (no VIN decode/ML prediction/sold summary — use comp median; Hot List and Avoid List are US-only; Pre-Auction VIN Check works with comp pricing). Confirm: "Using profile: [dealer.name], [ZIP], [Country]". All preference values from profile — do not re-ask.

User Context

Dealer (owner, buyer, inventory manager) making bid/no-bid decisions at auction in minutes; buying 15-40 units/month.

Field	Source	Default
ZIP, state, dealer_type, dealer_id	Profile	—
Target margin %, recon cost, radius	Profile	15%, $1,500, 75mi
Floor plan/day, max DOM	Profile	$35, 45 days

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. → Extract only: year, make, model, trim, body_type, drivetrain, engine, fuel_type, transmission. Discard full response.

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. → Extract only: predicted_price from each call. Discard full response.

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. → Extract only: total_count (supply), median_price, avg_dom from stats. Discard full response.

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. → Extract only: sold_count, average_days_on_market. Discard full response.

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 dealer: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, dealer_id, or source/domain), 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 the best available dealer identifier: dealer_id (preferred) or source (web domain) if dealer_id unavailable. Add 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. → Extract only: facet counts per body_type, make, fuel_type; total inventory count. Discard full response.

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. → Extract only: per body_type — sold_count. Discard full response.

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. → Extract only: per make — sold_count. Discard full response.

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

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. → Extract only: total_count (supply), avg_dom, avg_price from stats. Discard full response.

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. → Extract only: per make/model — sold_count. Discard full response.

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."

Output

Present: BUY/CAUTION/PASS verdict per VIN with max bid, retail value, expected turn, D/S ratio, projected net profit. Batch VINs as table. Hot/Avoid lists as ranked top-10. Category gaps as "Buy More Of" / "Slow Down On" with unit count recommendations. Optimize for phone readability at auction.