Plan future recurrence windows
Enumerate a bounded set of future recurrence slots from generic schedule data, including local-time daylight-saving policies and explicit coverage.
Give an explicit replay-stable now, recurring schedules, and max_occurrences_per_schedule. Returns future_occurrences in start order, rejected_schedules, skipped_resolutions, and per-schedule coverage showing count and calendar-scan limits. Recurrence is open-ended; a bounded plan never claims the schedule itself is exhausted. This is a pure plan: it writes no occurrence state, creates no materialization intents, and runs no action.
What it asks for
- now (Needed, value): Explicit replay-stable time: ISO text or epoch milliseconds.
- schedules (Needed, list): Generic timed-action recurrence schedule DATA, including timezone, cadence, local window, DST policies, and owner dispatch.
- max_occurrences_per_schedule (Needed, number): Maximum future occurrence rows returned for each valid schedule.
- max_calendar_scan_days (Optional, number): Bound the local calendar dates scanned per schedule.
Use it from your AI
Connect the AI you already use to Trillion once. Then ask in your own words; your AI finds this tool by what it does and runs it.
Say to your AI: “Use Trillion to enumerate a bounded set of future recurrence slots from generic schedule data, including local-time daylight-saving policies and explicit coverage”
What your AI calls: run_tool {"tool_id":"tool:plan-recurring-windows"}