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MCP Prompts

AgenticTime provides 4 built-in MCP prompts that agents can invoke for structured temporal reasoning.

`time_plan`

Generate a temporal plan for a project or task.

Arguments

Argument	Type	Required	Description
`goal`	string	Yes	What needs to be accomplished
`deadline`	string	No	Target completion date (ISO 8601)
`constraints`	string	No	Known constraints or blockers

Behavior

The prompt instructs the agent to:

Break the goal into sequential phases
Estimate durations for each phase with confidence intervals
Identify dependencies between phases
Flag any scheduling conflicts with existing commitments
Produce a structured timeline with milestones

Example

{
  "name": "time_plan",
  "arguments": {
    "goal": "Ship authentication module v2",
    "deadline": "2026-04-01T00:00:00Z",
    "constraints": "Team of 2, one week vacation in March"
  }
}

`time_review`

Review temporal health of the current project.

Arguments

Argument	Type	Required	Description
`scope`	string	No	`all`, `deadlines`, `schedules`, `sequences` (default: `all`)
`window_days`	number	No	Look-ahead window in days (default: 14)

Behavior

The prompt instructs the agent to:

List upcoming deadlines sorted by urgency
Identify overdue items
Check for scheduling conflicts in the window
Report on sequence progress (percent complete)
Flag any deadlines at risk based on remaining time vs. estimated effort
Suggest priority adjustments

Example

{
  "name": "time_review",
  "arguments": {
    "scope": "deadlines",
    "window_days": 7
  }
}

`time_estimate`

Estimate the duration of a task using historical data and heuristics.

Arguments

Argument	Type	Required	Description
`task`	string	Yes	Description of the task to estimate
`complexity`	string	No	`trivial`, `simple`, `moderate`, `complex`, `unknown` (default: `unknown`)
`similar_to`	string	No	Reference to a previous task for anchoring

Behavior

The prompt instructs the agent to:

Analyze the task description
Compare against historical duration estimates if available
Apply complexity multipliers
Produce a three-point estimate (optimistic, likely, pessimistic)
Calculate a confidence-weighted estimate
Store the estimate in the temporal graph

Example

{
  "name": "time_estimate",
  "arguments": {
    "task": "Migrate database from MySQL to PostgreSQL",
    "complexity": "complex"
  }
}

`time_schedule_day`

Plan and optimize the current day's schedule.

Arguments

Argument	Type	Required	Description
`date`	string	No	Date to plan (default: today, ISO 8601 date)
`work_hours`	number	No	Available work hours (default: 8)
`priorities`	string	No	Comma-separated priority focus areas

Behavior

The prompt instructs the agent to:

Load all schedules and deadlines for the target date
Identify fixed commitments (meetings, recurring schedules)
Calculate available free blocks
Suggest task assignments for free blocks based on deadline urgency
Flag if the day is overcommitted
Produce a minute-by-minute schedule recommendation

Example

{
  "name": "time_schedule_day",
  "arguments": {
    "date": "2026-03-01",
    "work_hours": 6,
    "priorities": "api-review, testing"
  }
}