schedule_self

Schedule a CONTINUATION of your own work to run later — the way to handle recurring or deferred tasks instead of looping in-process or busy-waiting. Pick exactly one cadence: when (one-shot RFC3339 UTC), interval_minutes (every N minutes), or cron (5-field expr; supports */N steps and @daily/@hou...

Server Yaver yaver-cli
Category Write
Risk class Medium
Parameters 101 required

What schedule_self does on Yaver

AI agents use schedule_self to create or update resources in Yaver — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Yaver environment.

ParameterTypeRequiredDescription
cron string 5-field cron expression (minute hour day month weekday), e.g. '*/30 * * * *' or '@daily'. Use this OR when OR interval_minutes.
memo string Optional notes carried verbatim into the next run's prompt (state, findings, where you left off).
when string One-shot run time, RFC3339 UTC (e.g. 2026-06-17T09:00:00Z). Use this OR interval_minutes OR cron.
model string Optional model override for the next run.
title string Optional label. Defaults to a truncation of prompt.
prompt string Yes The instruction the next run executes. Self-contained — the next process has no memory of the current turn.
resume boolean Recurring only: natively resume the previous run's session each fire (claude/glm/codex by session id, opencode by --continue) instead of starting cold. Default
runner string Runner for the next run: claude | codex | opencode | glm. Defaults to this agent's default runner.
max_runs integer Stop after this many fires (0 = use the 100-fire safety cap for recurring; one-shot ignores this).
interval_minutes integer Repeat every N minutes (minimum 1). Use this OR when OR cron.

Parameters from the server's own tool schema.

Why schedule_self needs a policy

An AI agent can call schedule_self faster than any human can review — one bad instruction and it creates or modifies resources in Yaver by the hundred, each call as confident as the last.

Risk signalsHigh parameter count (10 properties) · Bulk/mass operation — affects multiple targets

Questions about schedule_self

What does the schedule_self tool do? +

Schedule a CONTINUATION of your own work to run later — the way to handle recurring or deferred tasks instead of looping in-process or busy-waiting. Pick exactly one cadence: when (one-shot RFC3339 UTC), interval_minutes (every N minutes), or cron (5-field expr; supports */N steps and @daily/@hourly macros). The next run starts as a FRESH process (no memory of this turn) so put everything it needs into prompt and memo. memo is carried verbatim into the next run's prompt. runner defaults to this agent's default; pass it to pin claude/codex/opencode/glm. Recurring schedules without max_runs are capped at 100 fires. It is categorised as a Write tool in the Yaver MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.

What parameters does schedule_self accept? +

schedule_self accepts 10 parameters: cron, memo, when, model, title, prompt, resume, runner, max_runs, interval_minutes. Required: prompt. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on schedule_self? +

Register the Yaver MCP server in PolicyLayer and add a rule for schedule_self: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Yaver. Nothing to install.

What risk level is schedule_self? +

schedule_self is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.

Can I rate-limit schedule_self? +

Yes. Add a rate_limit block to the schedule_self rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.

How do I block schedule_self completely? +

Set action: deny in the PolicyLayer policy for schedule_self. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.

What MCP server provides schedule_self? +

schedule_self is provided by the Yaver MCP server (yaver-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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