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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

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2. Reschedule Event

Prompt: Move my yoga class on Thursday from 5:30pm to 7:30pm. Event Structure (update):

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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

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4. Add Recurring Event

Prompt:
Add a 15-minute check-in with my research assistant every Friday at 10am. Event Structure:

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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

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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

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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

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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.

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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)

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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 
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"
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