Run List Analyzer
BUY/CAUTION/PASS ratings for every VIN on your run list before the gavel drops
Purpose
Lane managers and auction executives walk into sale day with a run list and a gut feeling — but no systematic way to predict which VINs will sell cleanly, which will struggle, and what the overall event revenue looks like before the first hammer drops. The result is reactive pricing, poor lane sequencing, and avoidable no-sales that damage floor credibility with buyers.
Run List Analyzer processes every VIN on the run list through a multi-agent pipeline: VIN decode for exact specs, ML wholesale value prediction with local comparables, active supply check for that make/model, and velocity data from sold transactions. The output is a structured run list table with expected hammer price, sell-through probability (HIGH/MEDIUM/LOW), lane sequence recommendation, and fee revenue estimate per unit — plus an event-level revenue forecast vs target.
How It Works
Execution flow. MCP tool calls are shown inline on each step.
Spawns the auction-house:run-list-pricer agent with the full VIN list. Agent processes each VIN in parallel through decode, price prediction, supply check, and velocity check.
decode_vin_neovinCalls decode_vin_neovin per VIN to get exact year, make, model, trim, and body_type. Exact specs are required for accurate price prediction.
predict_price_with_comparablesCalls predict_price_with_comparables per VIN with miles, ZIP, and dealer_type=independent (wholesale proxy). Expected hammer = predicted_price × 0.92.
search_active_carsCalls search_active_cars by make/model/state to get active supply count and median price for demand context.
get_sold_summaryCalls get_sold_summary by make/model/state for prior month to get sold_count and average_days_on_market. Calculates D/S ratio = monthly_sold / active_supply.
Assigns: HIGH (D/S > 2.0, 90% probability), MEDIUM (D/S 1.0–2.0, 75%), LOW (D/S < 1.0, 60%). Flags no-sale risk: expected hammer < $3K or D/S < 0.5.
Sorts by sell-through probability then by expected hammer within each tier. HIGH sell-through and high-value units in early lanes to build bidder energy. LOW units in later lanes.
Aggregates: total consigned, predicted sell count, gross hammer, total fees, overall sell-through %, fee revenue vs target.
MCP Tool Calls
| Tool | Calls | Purpose |
|---|---|---|
decode_vin_neovin | 1 per VIN | Decode exact specs for each consigned vehicle |
predict_price_with_comparables | 1 per VIN | Wholesale value prediction with local comparables |
search_active_cars | 1–2 per unique model | Local supply count and median price |
get_sold_summary | 1 per unique model | Velocity data for D/S ratio |
Example Output
RUN LIST ANALYSIS: Dallas Auto Auction — March 2026 37 vehicles analyzed ══════════════════════════════════════════════════ LANE 1 RECOMMENDATIONS (high sell-through first) VIN Year/Make/Model Miles Mkt Med Est Hammer Sell-Through Fee Rev ─────────────── ────────────────────────── ────── ─────── ────────── ──────────── ─────── 1HGCV1F3XPA123 2021 Honda Accord EX-L 44,200 $21,400 $20,900 HIGH ✓ $1,672 2T1BURHE0JC004 2021 Toyota Camry SE 38,100 $22,800 $21,400 HIGH ✓ $1,712 5J6RS3H7XPL002 2022 Honda CR-V EX-L 51,300 $28,200 $26,900 HIGH ✓ $2,152 1C4RJFBG5MC882 2022 Jeep Grand Cherokee 62,400 $31,500 $29,900 MEDIUM ⚡ $2,392 3FADP4BJ7FM138 2019 Ford Focus SE 88,100 $10,800 $9,200 CAUTION ⚠ $736 LANE 2 (medium sell-through) [8 vehicles — avg hammer $18,400] LANE 3 (lower sell-through — engage late bidders) [4 vehicles — no-sale risk flagged: 2 units expected hammer < $4K] SUMMARY Total Consigned: 37 vehicles Predicted Sell Count: 32 vehicles (86.5% — above 85% target ✓) Predicted Gross Hammer: $714,200 Predicted Total Fees: $57,136 (buyer 5% + seller 3%) No-Sale Risk: 2 vehicles flagged TOP BUYS (highest spread to comp) 2021 Toyota RAV4 XSE — Est hammer $24,100 vs median $26,400 (+9.5%) 2020 Ford F-150 XLT — Est hammer $28,200 vs median $30,100 (+6.7%) 2022 Honda CR-V LX — Est hammer $21,800 vs median $23,200 (+6.4%) LANE SEQUENCE RECOMMENDATION Lanes 1–2: All HIGH sell-through units — build early energy Lane 3: MEDIUM units — momentum carries bidders Lane 4: Risk units + set realistic reserves
Cost Estimate
20 run lists (avg 30 VINs) ≈ $3–8
Limitations
- US: full workflow (VIN decode, ML pricing, supply, velocity). UK: supply snapshot only via search_uk_active_cars — no VIN decode or ML pricing.
- Price prediction accuracy varies by vehicle age, mileage, and local market depth — confidence intervals are wider for low-volume models.
- VIN list must be provided by user — skill does not source VINs independently.
- Multi-agent call count scales with run list size: 30-VIN list ≈ 80–120 API calls total.
More in the Auction House Plugin
Same Capability, Different Plugin
These skills share the same underlying methodology but are tuned for a different audience.