Cancel a running job.
AI agents call cancel_job to permanently remove resources in SPIRED-Stab MCP — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Cancelling a job stops an in-progress computation permanently. The job's intermediate state is lost and the operation cannot be undone, making this Destructive. The blast radius is medium: it wastes compute resources and requires resubmission, but does not delete stored data or financial assets.
From the tool's definition Cancel a running job — cancellation is irreversible; the running computation is terminated and cannot be resumed.
Attacks that exploit this kind of access
Cancel a running job. It is categorised as a Destructive tool in the SPIRED-Stab MCP MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the SPIRED-Stab 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 SPIRED-Stab MCP. 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 SPIRED-Stab MCP server (macromnex/spired_stab_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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