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.

Market Momentum Report — Market Context for Strategic Planning

Manufacturer Profile (Load First)

Load the marketcheck-profile.md project memory file if exists. Extract: brands (highlight with star), states, competitor_brands, country. Works without profile (national overview). US-only; if UK, inform not available. Confirm profile.

User Context

User is an OEM strategist or brand manager needing sector-level market context for strategic planning -- what is the overall market doing, and how does your brand fit within it?

Workflow: Monthly Market Momentum

Step 1 — Macro signals (total market)

Total volume: Call mcp__marketcheck__get_sold_summary with:

• date_from / date_to: current month
• ranking_dimensions: inventory_type
• ranking_measure: sold_count
• top_n: 5

Repeat for prior month and 3 months ago. → Extract only: sold_count, average_sale_price, average_days_on_market per inventory_type per period. Discard full response.

Calculate:

• Total units MoM %
• Avg transaction price MoM %
• New vs Used mix shift
• Industry-wide average DOM trend

EV penetration: Call with fuel_type_category=EV for current and prior. Calculate penetration rate and bps change. → Extract only: sold_count for EV per period. Discard full response.

Step 2 — Winners and losers (by market share)

Call mcp__marketcheck__get_sold_summary with:

• ranking_dimensions: make
• ranking_measure: sold_count
• ranking_order: desc
• top_n: 25
• Current month AND prior month → Extract only: make, sold_count per period. Discard full response.

Calculate share % and bps change for each make. Identify:

• Top 5 gainers (largest positive bps change)
• Top 5 losers (largest negative bps change)
• Your brand's position in the ranking and whether gaining or losing
• Competitor positions and their direction

Step 3 — Pricing power index

Call mcp__marketcheck__get_sold_summary with:

• inventory_type: New
• ranking_dimensions: make
• ranking_measure: price_over_msrp_percentage
• ranking_order: desc
• top_n: 20
• Current month → Extract only: make, price_over_msrp_percentage per brand. Discard full response.

Categorize:

• Above MSRP (still commanding premiums): count and avg premium %
• At MSRP (within +/-1%): count
• Below MSRP (discounting): count and avg discount %

Highlight where YOUR brands and COMPETITOR brands fall in the pricing power spectrum.

Track overall: what % of new vehicles sell above/below MSRP? Compare to prior month.

Step 4 — Depreciation alert

Call mcp__marketcheck__get_sold_summary with:

• inventory_type: Used
• ranking_dimensions: body_type
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 10
• Current month AND 3 months ago → Extract only: body_type, average_sale_price per period. Discard full response.

Calculate monthly depreciation rate per segment. Flag segments with > 1.5%/month as accelerating.

Also identify the 5 fastest depreciating specific models (by make/model):

• ranking_dimensions: make,model
• ranking_measure: average_sale_price
• ranking_order: asc
• top_n: 20 → Extract only: make, model, average_sale_price per period. Discard full response.

Cross-reference with 3-month-ago data. Flag any of YOUR models or COMPETITOR models in the list.

Step 5 — Regional price variance (optional, if state data available)

Call mcp__marketcheck__get_sold_summary with:

• summary_by: state
• ranking_measure: average_sale_price
• ranking_order: desc
• top_n: 10 → Extract only: state, average_sale_price, sold_count per state. Discard full response.

Focus on states in the user's profile if available.

Identify:

• Most expensive states (premium markets)
• Cheapest states (value markets)
• Price spread (highest vs lowest)

Step 6 — Supply health

Call mcp__marketcheck__search_active_cars with:

• car_type: new, stats: price,dom, rows: 0

And separately with car_type=used. → Extract only: num_found, stats.dom.mean per car_type. Discard full response.

Calculate:

• Total active new inventory nationally (or by state)
• Total active used inventory
• Implied days supply for each
• MoM inventory build/draw trend

Output

Present: macro signals table (volume, price, DOM, EV penetration, mix), winners/losers by share change (star your brands), pricing power index, depreciation alerts, optional regional snapshot, composite health signal (EXPANDING/STABLE/CONTRACTING/MIXED), and 3 strategic implications for your brand.

Composite Health Signal Logic

• EXPANDING: Volume up > 2%, pricing stable or rising, days supply < 60
• STABLE: Volume +/-2%, pricing +/-1%, days supply 50-75
• CONTRACTING: Volume down > 2%, pricing falling, days supply > 75
• MIXED: Conflicting signals (e.g., volume up but days supply building)

Important Notes

• US-only: Requires get_sold_summary for sold data.
• Use the most recent COMPLETE month. If today is March 5, "current month" = February.
• This is the broadest report — keep it scannable. OEM strategists need the market context in 30 seconds, then drill into brand-specific implications.
• Always bold or star your brands and highlight competitors in every table.
• Frame the final summary as strategic context: what does this market environment mean for YOUR brand's decisions?
• This report works well as a regular monthly deliverable. Suggest: "Want me to run this at the start of each month?"