Cancels an existing leave request.
AI agents call leave.cancel_request to retrieve information from Tlc Portal without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Even though leave.cancel_request only reads data, uncontrolled read access leaks sensitive information and racks up API costs — an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
Cancels an existing leave request. It is categorised as a Read tool in the Tlc Portal MCP Server, which means it retrieves data without modifying state.
Register the Tlc Portal MCP server in PolicyLayer and add a rule for leave.cancel_request: 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 Tlc Portal. Nothing to install.
leave.cancel_request is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the leave.cancel_request 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 leave.cancel_request. 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.
leave.cancel_request is provided by the Tlc Portal MCP server (mingovvv/tlc-portal-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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