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 Share Analyzer — Competitive Intelligence from Sold Data

Convert MarketCheck sold transaction data into real-time market share analytics. Track brand and model-level share, segment conquest patterns, dealer group performance, EV adoption curves, and regional demand distribution — all without waiting 60-90 days for traditional syndicated reports.

Dealer Group Profile (Load First -- Optional Context)

Load the marketcheck-profile.md project memory file. If exists, extract: group_name, locations[], preferences; from default location: state, franchise_brands, dealer_type, country. If missing, ask for fields. US-only (get_sold_summary); UK → not available. Confirm profile.

User Context

Dealer group executive, OEM analyst, or market researcher tracking brand/segment share, competitive positioning, and EV adoption from sold transaction data.

Auto-loaded from profile: state (geographic scope), franchise_brands (brand focus). Ask: time period (month range), comparison period, segment focus, inventory type (default Both). No geo specified → default national.

Workflow: Brand Market Share

Calculate market share by make for a given period and compare against a prior period to identify gainers and losers.

1. Call mcp__marketcheck__get_sold_summary for the current period:

   • date_from / date_to: target month first-to-last day
   • state: user's state filter (omit for national)
   • inventory_type: as specified (or omit for both)
   • ranking_dimensions: make
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 20 → Extract only: per make — sold_count, total sold_count. Discard full response.

2. Repeat for the prior period with identical filters but adjusted dates. → Extract only: per make — sold_count, total sold_count. Discard full response.

3. Calculate for each make:

   • Current Share % = Make Sold / Total Sold × 100
   • Prior Share % = same for prior period
   • Share Change (bps) = (Current % - Prior %) × 100
   • Volume Change % = (Current Sold - Prior Sold) / Prior Sold × 100

4. Present as a ranked table:

   • Columns: Rank, Make, Current Sold, Current Share %, Prior Sold, Prior Share %, Share Change (bps), Volume Change %
   • Sort by Current Share % descending
   • Highlight the group's franchise brands in bold
   • Flag makes gaining > 50 bps as "GAINING" and losing > 50 bps as "LOSING"

5. Add a summary paragraph: "The top 3 share gainers this period were [X], [Y], [Z], collectively picking up [N] basis points. The biggest losers were [A], [B], [C]. [Group's brand] moved from #X to #Y position with a [+/-N] bps shift."

Workflow: Segment Conquest Analysis

Determine which brands are winning within specific vehicle segments (body types) and identify conquest opportunities.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target period
   • state: user's state filter (omit for national)
   • body_type: target segment (e.g. SUV)
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 15 → Extract only: per make/model — sold_count. Discard full response.

2. Repeat for comparison period. → Extract only: per make/model — sold_count. Discard full response.

3. If the user wants multi-segment comparison, repeat step 1 for each body_type: SUV, Sedan, Pickup, Hatchback, Coupe, Van/Minivan.

4. For each segment, calculate:

   • Segment leader and their share %
   • Group's brand rank within the segment
   • Gap to leader in units and share points
   • Fastest gaining model in the segment (largest positive bps change)

5. Present per-segment tables:

   • Columns: Rank, Make, Model, Sold Count, Segment Share %, Prior Share %, Change (bps)
   • Also present a Segment Summary table: Segment, Leader, Leader Share %, Group Brand Rank, Group Brand Share %, Gap to Leader

6. Conquest insight with specific recommendations for each location's market.

Workflow: Dealer Group Benchmarking

Rank dealer groups by sales volume and operational efficiency to identify top performers and laggards. Useful for comparing the user's group against public peers.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target period
   • state: user's state filter (omit for national)
   • ranking_dimensions: dealership_group_name
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 20 → Extract only: per group — sold_count. Discard full response.

2. Same filters but ranking_measure: average_days_on_market, ranking_order: asc. → Extract only: per group — average_days_on_market. Discard full response.

3. Same filters but ranking_measure: average_sale_price, ranking_order: desc. → Extract only: per group — average_sale_price. Discard full response.

4. Merge the three result sets by dealership_group_name. Build a Dealer Group Leaderboard:

   • Columns: Rank (by volume), Dealer Group, Sold Count, Market Share %, Avg DOM, Avg Sale Price, Efficiency Score
   • Efficiency Score = Sold Count / Avg DOM (higher is better — moves more units faster)

5. If the user specifies a make, add a make filter to all calls to see dealer group performance within a single brand's network.

6. Highlight the user's own group in the leaderboard and provide comparative analysis.

Workflow: EV Adoption Tracking

Monitor electric and hybrid vehicle penetration rates over time against the total market.

1. Call mcp__marketcheck__get_sold_summary for EV sales:

   • date_from / date_to: target period
   • state: user's state filter (omit for national)
   • fuel_type_category: EV
   • ranking_dimensions: make,model
   • ranking_measure: sold_count
   • ranking_order: desc
   • top_n: 15 → Extract only: per make/model — sold_count; plus total EV sold_count. Discard full response.

2. Same filters but fuel_type_category: Hybrid. → Extract only: per make/model — sold_count; plus total Hybrid sold_count. Discard full response.

3. Call for total market (no fuel_type_category): ranking_dimensions: make, ranking_measure: sold_count, top_n: 1. → Extract only: total sold_count. Discard full response.

4. Repeat steps 1-3 for the prior period to calculate trend. → Extract only: same fields per period. Discard full response.

5. Calculate:

   • EV Penetration Rate = EV Sold / Total Sold x 100
   • Hybrid Penetration Rate = Hybrid Sold / Total Sold x 100
   • Combined Electrified Rate = (EV + Hybrid) / Total x 100
   • Period-over-period change for each rate

6. Present:

   • EV Penetration Summary
   • Top EV Models table
   • Top Hybrid Models table
   • EV Brand Share table

Workflow: Regional Demand Heatmap

Map sales volume and pricing by state for a specific make or model to reveal geographic demand patterns. Particularly useful for dealer groups operating in multiple states.

1. Call mcp__marketcheck__get_sold_summary with:

   • date_from / date_to: target period
   • make: target make (required), model: optional
   • summary_by: state, limit: 51 → Extract only: per state — sold_count. Discard full response.

2. If pricing context needed, add ranking_dimensions: make,model, ranking_measure: average_sale_price, summary_by: state, limit: 51. → Extract only: per state — average_sale_price. Discard full response.

3. Calculate for each state:

   • Volume rank (which states buy the most)
   • Price rank (where is it cheapest vs most expensive)
   • Price-to-national-average ratio

4. Present as a State-Level Demand Table sorted by sold count descending. Highlight states where the group has locations.

5. Summary with recommendations for each location's market.

Output

Present: competitive headline with share % and bps change, ranked share tables (volume + share % + change bps), comparison period data for trend context, and strategic implications for the dealer group (allocation shifts, cross-location opportunities). Cite data period and geography.