Clear all cached data from the extractor.
AI agents call clear_extractor_cache to permanently remove resources in YouTube MCP Server Enhanced — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Clearing a cache is an irreversible operation; once deleted, cached data is gone and must be re-fetched. While the original source data (YouTube) still exists, the cached state is permanently destroyed. This fits the Destructive category as it irreversibly deletes stored data.
From the tool's definition 'Clear all cached data from the extractor' — permanently removes all cached data, which cannot be recovered.
Risk signalsBulk/mass operation — affects multiple targets
Attacks that exploit this kind of access
Clear all cached data from the extractor. It is categorised as a Destructive tool in the YouTube MCP Server Enhanced MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the YouTube MCP Server Enhanced MCP server in PolicyLayer and add a rule for clear_extractor_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 YouTube MCP Server Enhanced. Nothing to install.
clear_extractor_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_extractor_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_extractor_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_extractor_cache is provided by the YouTube MCP Server Enhanced MCP server (labeveryday/youtube-mcp-server-enhanced). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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