Competitive Pricer — Market Price Context for Appraisals

Appraiser Profile (Load First)

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.

CPO Detection

When pricing a vehicle, determine if it is Certified Pre-Owned (CPO):

1. From user input: If the user states the vehicle is certified or CPO.
2. From listing data: If the vehicle listing includes is_certified=true.
3. From VIN history: If get_car_history shows the vehicle listed as certified.

When a vehicle IS CPO:

• Call predict_price_with_comparables with is_certified=true for the CPO market price
• Also call WITHOUT is_certified (or with is_certified=false) for the non-CPO market price
• Search comps with is_certified=true filter for apples-to-apples CPO comparables
• Calculate and display the CPO premium:

CPO 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 Context

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).

Workflow: Price-Check a Single VIN

Use this when the user says "price check this VIN" or "what's the market on this one."

1. 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.

2. Get predicted market prices (dual) — Make TWO calls to mcp__marketcheck__predict_price_with_comparables:

   • Franchise (retail): with vin, miles, zip, dealer_type=franchise. This represents full retail market value.
   • Independent (wholesale-proxy): with the same parameters but 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.

3. 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.

4. Calculate price position — If the user provided a subject price or asking price, compare it against the competitive set:

   • Percentile rank (e.g., "The subject price is lower than 72% of competing units")
   • Distance to the cheapest and most expensive unit
   • Median and mean market price
   • Number of competing units within +/- 5% of the subject price

5. 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

Workflow: Trade-In VIN Price History

Use this when the appraiser asks "what's the history on this VIN" or needs listing trajectory context before finalizing a valuation.

1. 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.

2. 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.

3. Get predicted price — Call mcp__marketcheck__predict_price_with_comparables with vin, miles, zip, dealer_type=franchise. → Extract only: predicted_price. Discard full response.

4. Analyze the trajectory — From the history, extract:

   • Number of dealers that have listed this VIN
   • Price at each listing and the direction of change
   • Total days on market across all listings
   • Whether the vehicle was ever listed as certified

5. 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.

Workflow: Market Price Distribution

Use this when the appraiser asks "what's the market look like for this model" or wants a statistical overview to anchor a valuation.

1. 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.

2. 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.

3. 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.

4. 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.

5. 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).

Output

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.