delete_schedule
AI agents call delete_schedule to permanently remove resources in Amazon SageMaker AI MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
The 'delete_' prefix is a strong indicator of destructive action that removes data irreversibly. Even without a description, schedule deletion would prevent future scheduled tasks from executing and cannot be undone without recreation. Classified as Destructive rather than Execute because the action itself (deletion) is irreversible by nature.
From the tool's definition Tool name 'delete_schedule' indicates deletion of a schedule resource. The verb 'delete' is explicitly destructive and irreversible. No description provided, but the tool name alone strongly indicates removal of data.
Attacks that exploit this kind of access
delete_schedule. It is categorised as a Destructive tool in the Amazon SageMaker AI MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Amazon SageMaker AI MCP Server MCP server in PolicyLayer and add a rule for delete_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 Amazon SageMaker AI MCP Server. Nothing to install.
delete_schedule is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.
Yes. Add a rate_limit block to the delete_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 delete_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.
delete_schedule is provided by the Amazon SageMaker AI MCP Server MCP server (awslabs.sagemaker-ai-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.