Cancel a running job and return its final status and output tail.
AI agents call cancel_job to permanently remove resources in Ns Hpc — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Cancelling a running job is irreversible — the job's execution is terminated and cannot be restarted from where it left off. Any in-progress computation, state, or output that wasn't saved is lost permanently. This is an irreversible destructive action with high blast radius if misused by an AI agent managing HPC workloads.
From the tool's definition Cancel a running job and return its final status and output tail.
Attacks that exploit this kind of access
Cancel a running job and return its final status and output tail. It is categorised as a Destructive tool in the Ns Hpc MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Ns Hpc MCP server in PolicyLayer and add a rule for cancel_job: 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 Ns Hpc. Nothing to install.
cancel_job 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_job 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_job. 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_job is provided by the Ns Hpc MCP server (li-yq/namespaced-hpc-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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