delete_instance

SERVERMcp SOURCEhttps://bigtableadmin.googleapis.com/mcp

Critical RISK CLASS

Category Destructive

Parameters 11 required

Recommended Hiddensee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/com-googleapis-bigtableadmin-mcp/delete-instance.md

WHAT IT DOES

What delete_instance does on Mcp

AI agents call delete_instance to permanently remove resources in Mcp, typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.

Parameter	Type	Required	Description
`name`	string	Yes	Required. The unique name of the instance to be deleted. Values are of the form `projects/{project}/instances/{instance}`.

Parameters from the server's own tool schema.

RISK

Why delete_instance is rated Critical

The delete_instance tool performs an irreversible deletion of a Bigtable instance, which cannot be undone. This is a destructive operation with potentially massive blast radius—an AI agent misusing this could wipe out production databases and cause severe data loss and business disruption.

From the tool's definition Tool name is 'delete_instance' and description states 'Delete an instance from a project.' This irreversibly removes a Google Bigtable instance and all associated data.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs delete_instance safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For delete_instance, this is the rule to start with:

delete_instance Hidden

delete_instance is removed from the agent's tool list entirely, so the agent never calls it. The rest of the server keeps working.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "hide": [
    "delete_instance"
  ]
}

RESTRICT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Mcp, apply this rule, and every delete_instance call is checked against it from then on.

FAQ

Questions about delete_instance

What does the delete_instance tool do? +

Delete an instance from a project. The request requires the 'name' field to be set in the format 'projects/{project}/instances/{instance}'. Example: { "name": "projects/my-project/instances/my-instance" } Before executing the deletion, you MUST confirm the action with the user by stating the full instance name and asking for "yes/no" confirmation. It is categorised as a Destructive tool in the Mcp MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.

What parameters does delete_instance accept? +

delete_instance accepts 1 parameter: name. Required: name. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on delete_instance? +

Register the MCP server in PolicyLayer and add a rule for delete_instance: 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 Mcp. Nothing to install.

What risk level is delete_instance? +

delete_instance 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? +

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

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

delete_instance is provided by the MCP server (https://bigtableadmin.googleapis.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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