Delete a data store from AWS HealthImaging.
AI agents call delete_datastore 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.
This tool permanently removes a datastore, which is an irreversible destructive action. It cannot be undone and results in loss of all data stored within that datastore. This is a classic Destructive category action (higher severity than Execute/Write). Confidence is high due to explicit 'delete' language and the nature of datastores as persistent data repositories.
From the tool's definition Tool name is 'delete_datastore' with description 'Delete a data store from AWS HealthImaging.' The verb 'delete' combined with 'datastore' indicates irreversible removal of data storage and its contents.
Attacks that exploit this kind of access
Delete a data store from AWS HealthImaging. 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_datastore: 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_datastore 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_datastore 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_datastore. 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_datastore 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.