delete_assignment
AI agents call delete_assignment to permanently remove resources in Clockify Time Tracking — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Despite the empty description reducing confidence slightly, the name 'delete_assignment' clearly signals an irreversible destructive action. In time-tracking systems, assignments represent work allocations that, once deleted, cannot be recovered. This falls squarely into the Destructive category (not merely Write) because deletion cannot be reversed.
From the tool's definition Tool name is 'delete_assignment' with an empty description. The verb 'delete' indicates irreversible removal of data. In the context of a time-tracking system, deleting an assignment cannot be undone and removes work allocation records.
Attacks that exploit this kind of access
delete_assignment. It is categorised as a Destructive tool in the Clockify Time Tracking MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Clockify Time Tracking MCP server in PolicyLayer and add a rule for delete_assignment: 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 Clockify Time Tracking. Nothing to install.
delete_assignment 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 delete_assignment 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 delete_assignment. 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.
delete_assignment is provided by the Clockify Time Tracking MCP server (pypi:clockify-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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