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

​
2. Reschedule Event

Prompt:

Move my yoga class on Thursday from 5:30pm to 7:30pm.

Event Structure (update):

​
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:

​
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

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"