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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 Bedrock Knowledge Base Retrieval MCP Server

AI agents call delete_instance_in_study to permanently remove resources in Amazon Bedrock Knowledge Base Retrieval 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 verb 'delete' paired with 'instance' indicates permanent removal of data or resources. Without a description, we rely on the naming convention, but 'delete' operations are categorized as Destructive per the rules since they cannot be undone. The 'in_study' qualifier suggests this operates on a research/knowledge base context where instances have value.

From the tool's definition Tool name contains 'delete_instance' which is irreversible data deletion; context shows AWS/Bedrock knowledge base operations where instances are likely substantive resources.

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 Bedrock Knowledge Base Retrieval 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 Bedrock Knowledge Base Retrieval 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 Bedrock Knowledge Base Retrieval 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 Bedrock Knowledge Base Retrieval 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 Bedrock Knowledge Base Retrieval 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 Bedrock Knowledge Base Retrieval MCP Server MCP server (awslabs.bedrock-kb-retrieval-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 Bedrock Knowledge Base Retrieval MCP Server tool call.

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

Free to start. No card required.

805 Amazon Bedrock Knowledge Base Retrieval MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.

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