clear_cache
AI agents call clear_cache to permanently remove resources in Teachermall — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
The name 'clear_cache' suggests it irreversibly deletes cached data. Cache clearing is generally not recoverable (the cached data is gone until regenerated), placing it in the Destructive category. However, since the description is empty, the actual behavior is unknown, lowering confidence.
From the tool's definition Tool name is 'clear_cache'; description is empty and uninformative.
Attacks that exploit this kind of access
clear_cache. It is categorised as a Destructive tool in the Teachermall MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Teachermall MCP server in PolicyLayer and add a rule for clear_cache: 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 Teachermall. Nothing to install.
clear_cache 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 clear_cache 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 clear_cache. 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.
clear_cache is provided by the Teachermall MCP server (reallygood83/teachermall-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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