Market price context for appraisals. Triggers: "price this car", "market price for this", "compare pricing", "price check VIN", "what's the market on this", market pricing context, price positioning analysis, or understanding where a vehicle falls in the current competitive landscape for appraisal purposes.
Load the marketcheck-profile.md project memory file if exists. Extract: zip/postcode, state/region, specialization, radius, country, min_comp_count. If missing, ask for ZIP and radius. US-only for full tooling (decode, predict, history); UK uses search_uk_active_cars/search_uk_recent_cars only (no VIN decode — ask for YMMT). Confirm profile.
Dual pricing: always report BOTH franchise and independent market prices.
When pricing a vehicle, determine if it is Certified Pre-Owned (CPO):
is_certified=true.get_car_history shows the vehicle listed as certified.When a vehicle IS CPO:
predict_price_with_comparables with is_certified=true for the CPO market priceis_certified (or with is_certified=false) for the non-CPO market priceis_certified=true filter for apples-to-apples CPO comparablesCPO Market Price: $XX,XXX (based on N certified comps)
Non-CPO Market Price: $XX,XXX (based on N total comps)
CPO Premium: +$X,XXX (+X.X%)
When a vehicle is NOT CPO, skip the CPO-specific calls and price normally.
User is an appraiser needing market price context for defensible valuations — competitive landscape, listing prices, and price positioning.
| Required | Field | Source |
|---|---|---|
| Yes | VIN or YMMT | Ask |
| Auto | ZIP, radius | Profile |
| Recommended | Mileage | Ask |
| Optional | Target/asking price | Ask |
VIN provided → decode first (US only; UK → ask for YMMT).
Use this when the user says "price check this VIN" or "what's the market on this one."
Decode the VIN — Call mcp__marketcheck__decode_vin_neovin with vin to confirm year, make, model, trim, body type, drivetrain, engine, and transmission. Present the decoded specs to the user for confirmation.
→ Extract only: year, make, model, trim, body_type, drivetrain, engine, transmission. Discard full response.
Get predicted market prices (dual) — Make TWO calls to mcp__marketcheck__predict_price_with_comparables:
vin, miles, zip, dealer_type=franchise. This represents full retail market value.dealer_type=independent. This provides wholesale-oriented context.
→ Extract only: predicted_price, comparable VINs/prices from each call. Discard full response.2a. CPO pricing (if applicable) — If the vehicle is CPO (detected per CPO Detection section above), make additional calls with is_certified=true for both franchise and independent predictions. Report CPO market price separately from non-CPO market price, and show the CPO premium.
→ Extract only: CPO predicted_price, non-CPO predicted_price, comp counts. Discard full response.
Pull competing active listings — Call mcp__marketcheck__search_active_cars with year, make, model, trim (from step 1), zip, radius=75, sort_by=price, sort_order=asc, rows=15, car_type=used. This returns the competitive set across all dealer types.
→ Extract only: per listing — price, miles, dom, dealer_name, dealer_type, distance. Discard full response.
Calculate price position — If the user provided a subject price or asking price, compare it against the competitive set:
Deliver the market context — Present both market prices for the appraiser to select the appropriate benchmark:
Franchise (Retail) Market Price: $XX,XXX (based on N comps)
Independent (Wholesale) Market Price: $XX,XXX (based on N comps)
Spread: $X,XXX (X.X%)
Active Comps in Market: N within [radius] miles
Use this when the appraiser asks "what's the history on this VIN" or needs listing trajectory context before finalizing a valuation.
Pull listing history — Call mcp__marketcheck__get_car_history with vin, sort_order=desc to get the full timeline of listings across dealers.
→ Extract only: per listing — date, dealer_name, price, dom, is_certified. Discard full response.
Decode the VIN — Call mcp__marketcheck__decode_vin_neovin with vin to get full specs.
→ Extract only: year, make, model, trim, MSRP. Discard full response.
Get predicted price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise.
→ Extract only: predicted_price. Discard full response.
Analyze the trajectory — From the history, extract:
Deliver price history context — Show the price trajectory, current market value, and flag any red flags (e.g., multiple dealer hops in a short period, steep price drops suggesting a problem unit). This context is critical for the appraiser's confidence assessment.
Use this when the appraiser asks "what's the market look like for this model" or wants a statistical overview to anchor a valuation.
Pull market stats — Call mcp__marketcheck__search_active_cars with year, make, model, zip, radius=100, car_type=used, stats=price,miles, rows=0. The rows=0 returns only stats without individual listings.
→ Extract only: mean, median, min, max, stddev for price and miles, total count. Discard full response.
Pull the cheapest listings — Call mcp__marketcheck__search_active_cars with the same filters plus sort_by=price, sort_order=asc, rows=5.
→ Extract only: per listing — price, miles, dealer_name, dom. Discard full response.
Pull the most expensive listings — Call mcp__marketcheck__search_active_cars with the same filters plus sort_by=price, sort_order=desc, rows=5.
→ Extract only: per listing — price, miles, dealer_name, dom. Discard full response.
Present the distribution — Show: mean, median, min, max, standard deviation for price and miles. Identify the price bands (quartiles) and show where the subject vehicle would fall. This statistical context supports the appraiser's valuation methodology.
Highlight outliers — Flag any listings priced more than 2 standard deviations from the mean as potential data quality issues or unique units (salvage, high miles, rare trim).
Present: summary headline with vehicle and market price, data table(s) of comparables with price/miles/DOM/dealer, key market signals (price position, spread, comp count), and actionable recommendation for the appraiser's valuation.