Use Case: Scheduled Tasks and Timed Automation

The Problem

During long development sessions, you need the AI to do things at specific times: check build status in 10 minutes, remind you about a meeting, run a test suite after lunch. External cron jobs are overkill. You want scheduling inside the session.

Solution

The schedule MCP tool lets the AI (or you) schedule messages that fire at specific times and get processed automatically.

Basic: Timed Reminders

> Schedule a reminder in 30 minutes to check the CI build

AI calls: schedule(command="add", when="in 30m",
          message="Check the CI build status and report any failures",
          description="CI build check")

# 30 minutes later, the message fires automatically
# AI processes it: reads CI status, reports results

Time-of-Day Scheduling

> At 3pm, summarize everything we've done today

AI calls: schedule(command="add", when="3:00pm",
          message="Summarize all changes made in this session today",
          description="Daily summary")

If the time has already passed today, it fires tomorrow.

Absolute Datetime

> On March 30th at 10am, remind me to deploy the release

AI calls: schedule(command="add", when="2026-03-30 10:00",
          message="Time to deploy the release. Run the deployment checklist.",
          description="Release deployment")

Build-Check-Fix Loop

A practical pattern: schedule a build check, then fix issues when they appear.

> Start the long-running test suite in the background, then check results in 5 minutes

AI:
  1. shell(command="cargo test --release 2>&1 > /tmp/test-output.txt &", background=true)
  2. schedule(command="add", when="in 5m",
             message="Read /tmp/test-output.txt and report test results. If any tests failed, analyze the failures and suggest fixes.",
             description="Test results check")

# Session continues with other work...
# 5 minutes later: AI reads results, reports, suggests fixes

Monitoring Pattern

Chain scheduled checks to monitor something over time:

> Monitor the server logs for errors every 10 minutes for the next hour

AI:
  schedule(command="add", when="in 10m",
    message="Check server logs for errors. If found, report them. Then schedule another check in 10 minutes. Stop after 6 total checks.",
    description="Log check 1/6")

# Each check fires, AI processes it, and can schedule the next one
# (Each schedule is one-shot; the AI re-schedules in response to each fired message)

Managing Schedules

> What's scheduled?

AI calls: schedule(command="list")
# Shows all pending entries with IDs, trigger times, and countdown

> Cancel the deployment reminder

AI calls: schedule(command="remove", id="abc12345")

> Push the build check back to 20 minutes

AI calls: schedule(command="edit", id="def67890", when="in 20m")

Direct Control: /schedule Slash Command

The same operations are available as a session command, so you can list, add, edit, and remove schedules without going through the AI:

/schedule                                                 # list pending
/schedule add when="in 5m" message="check the build"
/schedule add when="9am" message="standup" every="1d" description="daily"
/schedule edit abc12345 when="in 1h"
/schedule remove abc12345

Multi-word values must be quoted (when="in 1h 30m", message='hello world'). See Session Commands → /schedule for the full reference.

Daemon + Scheduled Tasks

Combine with daemon mode for long-running automated workflows:

# Start daemon session
octomind run --name project-monitor --daemon --format jsonl

Then inject a setup message:

echo "Set up monitoring: check git status every 30 minutes, summarize changes, and alert if there are uncommitted changes older than 2 hours" | octomind send --name project-monitor

The AI schedules recurring checks within the session, each check can schedule the next.

Supported Time Formats

Limitations

• Session-scoped: Schedules belong to the session that created them. When resuming a session, scheduled entries are restored from the session event log.

Key Points

• schedule is a built-in MCP tool -- the AI uses it naturally in conversation
• The same operations are exposed as the /schedule slash command for direct control
• Messages fire automatically and the AI processes them like any user message
• One-shot entries fire once; entries with every re-schedule themselves automatically until removed
• Combine with daemon mode for persistent monitoring
• Use schedule(command="list") or /schedule to see all pending entries
• The session stays alive until all scheduled messages have fired