Cancel a running or queued evaluation.
AI agents call cancel_evaluation to permanently remove resources in Tuning Engines - LLM Fine-Tuning — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
An AI agent that decides to call cancel_evaluation doesn't hesitate, doesn't double-check, and doesn't stop at one. Whatever it removes from Tuning Engines - LLM Fine-Tuning is gone — there is no undo for destructive operations.
Attacks that exploit this kind of access
Cancel a running or queued evaluation. It is categorised as a Destructive tool in the Tuning Engines - LLM Fine-Tuning MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Tuning Engines - LLM Fine-Tuning MCP server in PolicyLayer and add a rule for cancel_evaluation: 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 Tuning Engines - LLM Fine-Tuning. Nothing to install.
cancel_evaluation 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 cancel_evaluation 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 cancel_evaluation. 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.
cancel_evaluation is provided by the Tuning Engines - LLM Fine-Tuning MCP server (tuningengines-cli). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.