compare_laps
ChatGPTCompares two laps (possibly from different sessions) with a per-sector time delta table computed server-side from GPS telemetry — the cheap way to answer 'where am I losing time'. Sectors are equal-distance splits of each lap's GPS distance, not official timing sectors. Lap numbers default to each session's best lap. Positive sector delta means lap B was slower there.
get_lap_telemetry
ChatGPTGPS telemetry trace for one lap of a session. Default response is downsampled to roughly one point per 10 m of travelled distance with [timeMs, distanceM, speedKph, lat, lon] per point — compact enough to analyze in-context. Set raw=true only when you genuinely need full resolution and every channel (altitude, g-forces, rpm); it is much larger. Omit lapNumber to get the session's best lap.
get_photos
ChatGPTReturns the user's photos for one entity as viewable images (downscaled JPEGs), so wear and condition can be assessed visually — e.g. brake disc lips/scoring or tire surface on a component installation, paddock/car photos on a session, detail shots on a setup. Returns the newest photos first (default 4, max 10 per call) with a metadata block per photo. Entities with photos are flagged via photoCount in list_vehicles and get_session.
get_session
ChatGPTFull context for one session: all laps with times and validity, the complete setup snapshot (tires, pressures, brakes, suspension, alignment, fuel), weather/track conditions, wear state of the tire and brake sets that were installed, session notes, linked onboard video links (YouTube), and a telemetry availability summary. Use list_sessions first to find the session id. Session, setup, and installed components each carry a photoCount; when > 0, get_photos returns the actual photos as viewable images.
get_track_day
ChatGPTWhole track day in one call: date, track, vehicle, organizer, day-level weather and notes (e.g. official results the driver wrote down), day photo count (get_photos scope 'trackday' returns the images — often timing-board or results screenshots), onboard video links, and a summary of every session that day (lap count, best lap, telemetry availability, photo count). Use the trackDayId from list_sessions or get_session. Drill into one session with get_session for setup, laps and wear context.
list_sessions
ChatGPTLists the user's track sessions, newest first. Filter by track name (substring match), vehicle id, and/or date range. Each entry has the session id (use with get_session, get_lap_telemetry, compare_laps), date, track, vehicle, lap count, best lap time, weather, whether GPS telemetry is available and whether onboard video links exist.
list_vehicles
ChatGPTLists the user's vehicles with total track mileage, the most recent setup snapshot (tires, pressures, alignment, brakes), and currently installed wear-tracked components (tire sets, brake pads, brake discs) including km/laps accrued and latest tread depth measurements. Each component has a photoCount; when > 0, get_photos(scope='component', id) returns viewable wear photos of that set.
