delete_series_by_uid
AI agents call delete_series_by_uid 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 tool performs data deletion, which is irreversible and cannot be undone. This qualifies as Destructive rather than Write. High severity is appropriate because deletion of AI/ML series (likely training data, model artifacts, or experimental runs in a SageMaker context) could result in permanent loss of valuable work.
From the tool's definition Tool name 'delete_series_by_uid' contains the verb 'delete', which indicates irreversible removal of data. The UID parameter suggests targeted deletion of a specific series object.
Attacks that exploit this kind of access
delete_series_by_uid. 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_series_by_uid: 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_series_by_uid 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_series_by_uid 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_series_by_uid. 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_series_by_uid 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.