AI agents use update_schedule to create or update resources in AdButler — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your AdButler environment.
| Parameter | Type | Required | Description |
|---|---|---|---|
id | number | Yes | Schedule ID |
end_at | string | — | End date/time in ISO-8601 format |
end_date | string | — | End date (YYYY-MM-DD HH:MM:SS) — deprecated, use end_at |
start_at | string | — | Start date/time in ISO-8601 format |
quota_type | string | — | Quota measurement type |
start_date | string | — | Start date (YYYY-MM-DD HH:MM:SS) — deprecated, use start_at |
day_cap_type | string | — | Daily cap type |
day_cap_limit | number | — | Daily cap limit |
day_parting_id | number | — | Day parting ID for time-of-day targeting |
quota_lifetime | number | — | Total quota amount (views or clicks, not per thousand) |
delivery_method | string | — | Delivery pacing: "default" (ASAP) or "smooth" (evenly distributed) |
end_at_timezone | string | — | IANA timezone for end_at |
Parameters from the server's own tool schema.
This tool modifies an existing schedule reversibly—updates can be changed again or reverted. It does not delete (Destructive), execute arbitrary code (Execute), create financial obligations (Financial), or retrieve data without side effects (Read). The moderate severity reflects that schedule changes in an ad management system could affect campaign delivery and business operations, but the effect is not irreversible.
From the tool's definition Tool name 'update_schedule' and description 'Update an existing schedule' indicate modification of existing data. The sibling tools include archive operations (destructive) and bulk operations (write), confirming this is a data modification context.
Risk signalsHigh parameter count (16 properties)
Attacks that exploit this kind of access
Update an existing schedule. It is categorised as a Write tool in the AdButler MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
update_schedule accepts 12 parameters: id, end_at, end_date, start_at, quota_type, start_date, day_cap_type, day_cap_limit, day_parting_id, quota_lifetime, delivery_method, end_at_timezone. Required: id. The full parameter table on this page comes from the server's own tool schema.
Register the AdButler MCP server in PolicyLayer and add a rule for update_schedule: 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 AdButler. Nothing to install.
update_schedule is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the update_schedule 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.
Set action: deny in the PolicyLayer policy for update_schedule. 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.
update_schedule is provided by the AdButler MCP server (adbutler/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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