analyze_flight_usage
ChatGPTHow an aircraft is flown across seven flight categories (training, business, pleasure, etc.) relative to a comparable cohort. Useful for appraisal narratives, insurance underwriting, and validating seller claims about usage.
assess_flight_activity_risk
ChatGPTForward-looking probability that an aircraft will fly little over the next ~180 days, as of a given date. Used to flag stagnant inventory, prioritize inspections, and risk-score collateral.
count_aircraft
ChatGPTAggregate registry census for a filter set: the total number of matching aircraft plus breakdowns by US state and by build year — in one call, without paging individual tails. Directly answers "how many Cessna R182s are registered in Oregon" (filter make_model_id + state, then read total_count / state_counts). Uses the same filter grammar as search_aircraft. Counts are computed over up to fleet_cap aircraft.
create_valuation_report
ChatGPTGenerate a full Windsock verified value report from instant valuation data (contemporary model only). Creates a durable report owned by your account and queues PDF generation. Requires Windsock PRO or Enterprise.
estimate_cost_of_ownership
ChatGPTAnnual/monthly operating-cost breakdown for a specific tail, combining registry data, recent flight patterns, and optional override assumptions (fuel, insurance, hangar, annual hours, etc.). Great for pre-buy disclosures and TCO comparisons.
find_similar_aircraft
ChatGPTCanonically similar aircraft (same make/model cohort and equipment profile) to a given tail — for comp tables, substitute inventory, and "aircraft like this" lists.
forecast_fuel_price
ChatGPTForward avgas/jet fuel price projection for an airport over 1–15 day horizons, with uncertainty bands. For trip budgeting and FBO economics.
get_account_usage
ChatGPTThe authenticated account's tier, included quotas, current-month usage, and estimated cost. Use to check entitlements before firing paid tools.
get_airport_weather
ChatGPTCombined current weather (METAR) and forecast (TAF) for an airport by ICAO/ident — for trip planning context.
get_market_metric
ChatGPTHistorical actuals and optional forward forecast (with 75/95/99% confidence bands) for a named market metric, optionally scoped to an aircraft segment. Provides market context to complement individual valuations.
get_report
ChatGPTFetch one report by id. Use include to inline sub-blocks in a single response, e.g. ["pricing","aircraft","components","inspection","appraiser","client","contents"].
get_report_section
ChatGPTFetch one of a report's structured sub-resources: comparable-sold, market-strength, interpretability, price-timeline, flight-review, flight-history, comparable-aircraft-stats, report-summary, pricing, aircraft, or logbooks. Mirrors the sections of the PDF report.
get_tracked_aircraft_snapshots
ChatGPTHistorical valuation snapshots for a watchlisted tail (dated value points). Use for value trends, portfolio appreciation/depreciation, and collateral monitoring over time.
impute_aircraft_specs
ChatGPTML fill-in for missing airframe, engine, and condition fields (SMOH, AFTT, interior/exterior quality, etc.) before a valuation. Identify the aircraft by registration or make_model_id; any fields you supply are preserved. Quality scores use a 1–10 scale.
list_market_categories
ChatGPTAircraft segment keys (e.g. single_engine_piston) used to scope market metrics. Pass a key as the category argument to get_market_metric.
list_market_metrics
ChatGPTCatalog of macro GA market metrics Windsock tracks (stable identifiers, labels, units, and whether each supports forecasting). Discover valid metric names before calling get_market_metric.
list_tracked_aircraft
ChatGPTList the tails on your watchlist with their latest tracking state.
lookup_aircraft
ChatGPTCanonical FAA registry + distilled aircraft profile for an N-number. Returns structured aircraft data plus make_model_id and avionics_ids — the IDs needed downstream for valuation and reference lookups. The first step in any "value this tail" workflow.
search_aircraft
ChatGPTRegistry-wide aircraft search — the fleet / market-research search surface. Returns individual registered tails matching an AND-combined filter set (make/model, US state, city, year, tail number, on-market status, or geographic radius), with pagination and sorting. Resolve a model name to a make_model_id with search_make_models first. For population totals (e.g. "how many R182s in Oregon") prefer count_aircraft, which avoids paging. Each filter is {field_name, condition, value}.
search_airworthiness_directives
ChatGPTSearch FAA Airworthiness Directive records, optionally filtered by make_model_id and compliance status. For pre-buy AD review, maintenance planning, and appraisal compliance sections.
search_avionics
ChatGPTSearch avionics equipment names/types, returning avionics_ids that affect equipment-adjusted valuations. Use to map panel/upgrade text to canonical ids.
search_logbooks
ChatGPTSemantic/hybrid search over digitized aircraft logbook entries across reports you can access. Each hit includes date, summary, OCR text, extracted fields, and page ids for citation. Scope to a report with report_id.
search_make_models
ChatGPTText search over FAA make/model names, returning make_model_id values needed by value_aircraft, impute_aircraft_specs, and reference lookups. Use to resolve "2015 Cirrus SR22" to a canonical id when no tail number is available.
search_reports
ChatGPTPaginated search over appraisal/valuation reports your account can access. Filter by registration, year, make_model_id, price bounds, and whether logbooks are attached.
search_stcs
ChatGPTSearch STC records, optionally filtered by make_model_id — certificate numbers, descriptions, applicability. For pre-buy due diligence and documenting mods that affect value.
track_aircraft
ChatGPTAdd a tail to your portfolio watchlist for recurring valuation snapshots and change monitoring. Requires make_model_id and aircraft_info.year; registration identifies the tail.
value_aircraft
ChatGPTMachine-generated aircraft market value at a single point in time, from today back to 1960. Provide a tail number OR a full spec (make_model_id + aircraft_info). Optional as_of_date selects the contemporary model (>= 2020-01-01 or omitted) or the deep-history longitudinal model (before 2020). Set include=["explanation"] or ["uncertainty"] for richer output. The flagship valuation tool.
