AuctionScout

Add an auction to the user's wishlist. Idempotent. Auto-creates a reminder (1 day before, falling back to 1 hour).

add_to_wishlist

Recent nearby auction results (not market sales), area auction statistics, risk factors, and LTR/BRRRR/Flip/STR deal metrics for one auction, from the same engine as the AuctionScout map pins. `deal` gives each strategy's status (a reason when it is pending), the discount to market, the inputs used and warnings. Numbers only, no AI narrative. deal_metrics.brrrr_cash_on_cash of 999 means all cash is recovered at refinance (deal.brrrr.all_cash_out is true).

analyze_auction

Create a reminder for an auction. minutes_before must be 15, 60, or 1440.

create_reminder

Save a named search with filters. `filters` is a predictions-filter object — see the schema for the exact fields. Range filters such as market_price_range are 2-element [min, max] arrays (e.g. [0, 500000]), not {min, max} objects. Daily new-match alerts (email + push) are on by default; pass alerts_enabled=false to opt out. Alert frequency is configured in the portal. Check the result's ignored_keys and ignored_values: anything listed there was NOT saved, so tell the user.

create_saved_search

Delete one of the user's auction reminders. Get the reminder_id from list_reminders (the `id` field) and confirm with the user before deleting. Returns deleted=false if no such auction reminder exists for this user.

delete_reminder

Full detail for one auction, including sold_price for completed sales, parcel id, county auditor/clerk URLs, and a (DB-only) estimates bundle with the market value (purchase_price: the resale value, not the buy price) and the model's predicted winning bid (predicted_price). Estimate fields may be null.

get_auction

Upcoming auctions with model-predicted sale prices and confidence scores. Auctions without a prediction row are included with prediction=null. Filter by state, county, min_confidence, or max_days_ahead. Up to 50 rows; total_count is the full match count, and a coverage object (same as search_auctions) says when a state isn't covered. ROI = (market_value - predicted_price) / predicted_price * 100, where market_value is the same market estimate search_auctions and get_auction return (latest market price estimate, else the automated home-value estimate). Cancelled, withdrawn, dismissed, vacated, redeemed and already-decided auctions (sold, no sale, etc.) are excluded; postponed and bankruptcy rows stay, labeled by sale_status. Each row carries state and sale_status. is_fallback=true means the price is a formula (market estimate times a historical range edge, or a $1,000 / $10M safety limit), not a model estimate; it carries low confidence and a fallback_reason. market_checked=false means the model price was made before the property had a market estimate, so nothing checked it (null for older rows).

get_predictions

Cached neighborhood data for an auction (looked up by auction_id). Sections: schools (district name, PI rating), amenities (nearby grocery/dining/parks/etc. and walkability score), demographics (census tract income/population/home values). Returns null for any section not yet in cache — no live API calls are made. If the school district isn't cached, schools instead falls back to the auction record's nearby-school rating (source: auction_record). Each missing or fallback section is explained in missing_reasons (no_coordinates, not_cached_yet, cache_expired, read_error). missing_reasons, not partial, is the complete signal: partial stays false when schools comes from the fallback.

get_neighborhood

The user's auction reminders. Call with NO arguments to list every reminder they have; pass auction_id to list only that auction's reminders. Each row includes the auction address and date.

list_reminders

Return the authenticated user's saved searches.

list_saved_searches