> ## Documentation Index > Fetch the complete documentation index at: https://docs.runtimelabs.ai/llms.txt > Use this file to discover all available pages before exploring further. # Prompt Examples and Event Structure **Prompt:**\ Schedule a meeting with the dog walker on Thursday April 24th at 2pm in Madison Square Park. **Event Structure:** * Summary: Meeting with dog walker * Start: 2025-04-24T14:00:00 * End: 2025-04-24T14:30:00 * Location: Madison Square Park * Description: (optional) * UID: [dog\_walker\_20250424@memorysystem.ai](mailto:dog_walker_20250424@memorysystem.ai) *** ### 2. **Reschedule Event** **Prompt:** Move my yoga class on Thursday from 5:30pm to 7:30pm. **Event Structure (update):** * Summary: Yoga class * Start: 2025-04-24T19:30:00 * End: 2025-04-24T20:30:00 * UID: [yoga\_class\_20250424@memorysystem.ai](mailto:yoga_class_20250424@memorysystem.ai) *** ### 3. **Delete Event** **Prompt:** Cancel the all-team meeting on Wednesday at 2pm. **Agent Task:** * Locate event by summary + timestamp * Delete via Calendar API using UID *** ### 4. **Add Recurring Event** **Prompt:**\ Add a 15-minute check-in with my research assistant every Friday at 10am. **Event Structure:** * Summary: Check-in with RA * Start: 2025-04-25T10:00:00 * End: 2025-04-25T10:15:00 * Recurrence Rule: FREQ=WEEKLY;BYDAY=FR * UID: [ra\_checkin\_20250425@memorysystem.ai](mailto:ra_checkin_20250425@memorysystem.ai) *** ### 5. **Soft Scheduling / Suggestion** **Prompt:**\ Find 30 minutes tomorrow afternoon for a deep work session. **Agent Task:** * Parse window: 2025-04-24T13:00:00 to 17:00:00 * Search for free time slot * Add tentative calendar entry *** ### 6. **Multi-Action Update** **Prompt:**\ Push the chemistry tutoring to Saturday morning and move my call with Alex to Sunday night. **Agent Task:** * Identify and reschedule two events * Event 1: * Summary: Chemistry tutoring * New Time: 2025-04-26T09:00:00 * Event 2: * Summary: Call with Alex * New Time: 2025-04-27T21:00:00 *** ### 1. **Reflects Real Agent Usage** Most meaningful interactions with calendar agents will begin from **natural language prompts**, not direct `.ics` inputs or structured tool calls. * You’re simulating actual usage patterns: "schedule this", "move that", "when is..." * It gives you a **direct path from interface → intent → execution** ### 2. **Tightly Couples Input with Outcome** * You can easily test whether: * The prompt is interpreted correctly * The right event is created/modified/deleted * The output aligns with expected `.ics` structure This makes it ideal for both **unit tests** and **end-to-end agent simulations**. ### 3. **Enables Dataset Generation + Fine-tuning** * You can generate a dataset of (prompt → event structure) pairs * Fine-tune or supervise the Cursor Calendar agent with examples like: * Input: “Cancel my sync with Max tomorrow” * Target JSON: (event details to delete) ### 4. **Gives You Flexibility to Inject Memory** Prompt-centric workflows let you condition agent behavior on prior traces: * “Didn’t I already book lunch on Thursday?” * “Move that reflection I wrote after the RA meeting” This ties directly into the **MCP memory layer**. By grounding your agent in conversational prompts, you: * Build a usable **interface abstraction** * Keep your pipeline modular (chat → intent → planning tool → calendar) * Allow future agents (Claude, GPT, custom planner) to act through MCP in a consistent way **Develop a lightweight conversation-to-action test harness**, where each test includes: * `user_input`: natural language prompt * `expected_tool_call`: e.g., `sync_traces_to_google([event])` * `expected_event`: minimal structured event block Use this both for: * MCP simulation testing * Agent tool call grounding (especially for Cursor/GPT/Claude) Schedule, reschedule, delete, schedule to a specific calendar, suggested schedule (light), set recurring, end recurring, add collaborator, add location, change location Recall, summarize Retrieve md format for editable interface ```yaml theme={null} tests: - id: create_event_dogwalker user_input: "Schedule a meeting with the dog walker on Thursday April 24th at 2pm in Madison Square Park." expected_tool: convert_trace_to_ics expected_event: summary: "Meeting with dog walker" start: "2025-04-24T14:00:00" end: "2025-04-24T14:30:00" location: "Madison Square Park" uid: "dog_walker_20250424@memorysystem.ai" - id: reschedule_yoga user_input: "Move my yoga class on Thursday from 5:30pm to 7:30pm." expected_tool: sync_traces_to_google expected_event: summary: "Yoga class" start: "2025-04-24T19:30:00" end: "2025-04-24T20:30:00" uid: "yoga_class_20250424@memorysystem.ai" - id: delete_team_meeting user_input: "Cancel the all-team meeting on Wednesday at 2pm." expected_tool: delete_event_by_uid expected_event: summary: "All-team meeting" start: "2025-04-23T14:00:00" uid: "team_meeting_20250423@memorysystem.ai" - id: recurring_checkin user_input: "Add a 15-minute check-in with my research assistant every Friday at 10am." expected_tool: convert_trace_to_ics expected_event: summary: "Check-in with RA" start: "2025-04-25T10:00:00" end: "2025-04-25T10:15:00" rrule: "FREQ=WEEKLY;BYDAY=FR" uid: "ra_checkin_20250425@memorysystem.ai" - id: soft_schedule user_input: "Find 30 minutes tomorrow afternoon for a deep work session." expected_tool: suggest_open_timeslot expected_event: summary: "Deep work session" duration_minutes: 30 time_window: start: "2025-04-24T13:00:00" end: "2025-04-24T17:00:00" - id: multi_reschedule user_input: "Push the chemistry tutoring to Saturday morning and move my call with Alex to Sunday night." expected_tool: sync_traces_to_google expected_events: - summary: "Chemistry tutoring" start: "2025-04-26T09:00:00" end: "2025-04-26T10:00:00" uid: "tutoring_20250426@memorysystem.ai" - summary: "Call with Alex" start: "2025-04-27T21:00:00" end: "2025-04-27T22:00:00" uid: "call_alex_20250427@memorysystem.ai" ```