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delete_instance_in_study

delete_instance_in_study

How to control delete_instance_in_study ↓

What delete_instance_in_study does on Amazon Location Service MCP Server

AI agents call delete_instance_in_study to permanently remove resources in Amazon Location Service MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.

Critical Risk

Why delete_instance_in_study needs a policy

The 'delete' operation in the tool name signifies that this tool performs a destructive action that cannot be undone. While the description is empty and provides no additional context, the function name itself is sufficiently clear that it removes an instance from a study, which is an irreversible operation affecting data integrity.

From the tool's definition Tool name 'delete_instance_in_study' contains the verb 'delete', which indicates irreversible removal of data.

Documented attack patterns abuse exactly the kind of access delete_instance_in_study gives an agent:

How to control delete_instance_in_study

PolicyLayer is an MCP gateway — it sits between your AI agents and Amazon Location Service MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_instance_in_study:

policy.json
{
  "version": "1",
  "default": "deny",
  "hide": [
    "delete_instance_in_study"
  ]
}

delete_instance_in_study disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.

  1. Create a free account and register Amazon Location Service MCP Server — nothing to install.
  2. Add this policy — paste it, or build it visually.
  3. Point your MCP client (Claude, Cursor, anything) at your gateway URL.
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Related tools and policies

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Questions about delete_instance_in_study

What does the delete_instance_in_study tool do? +

delete_instance_in_study. It is categorised as a Destructive tool in the Amazon Location Service MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

How do I enforce a policy on delete_instance_in_study? +

Register the Amazon Location Service MCP Server MCP server in PolicyLayer and add a rule for delete_instance_in_study: 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 Location Service MCP Server. Nothing to install.

What risk level is delete_instance_in_study? +

delete_instance_in_study is a Destructive tool with critical risk. Critical-risk tools should be blocked by default and only enabled with explicit human approval.

Can I rate-limit delete_instance_in_study? +

Yes. Add a rate_limit block to the delete_instance_in_study 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 delete_instance_in_study completely? +

Set action: deny in the PolicyLayer policy for delete_instance_in_study. 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 delete_instance_in_study? +

delete_instance_in_study is provided by the Amazon Location Service MCP Server MCP server (awslabs.aws-location-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Amazon Location Service MCP Server tool call.

Start from Amazon Location Service MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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805 Amazon Location Service MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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