Remove all pending jobs from a Serverless endpoint queue. Only affects queued jobs — in-progress jobs continue running. Use this for error recovery or clearing outdated requests.
AI agents call purge-endpoint-queue to permanently remove resources in RunPod MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool permanently deletes queued jobs without recovery mechanism. While it does not delete the endpoint itself or affect running jobs, the irreversible removal of all pending jobs in a queue meets the Destructive category definition.
From the tool's definition Tool name contains 'purge' and description states 'Remove all pending jobs from a Serverless endpoint queue' — purge is an irreversible deletion operation that cannot be undone.
Documented attack patterns abuse exactly the kind of access purge-endpoint-queue gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and RunPod MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for purge-endpoint-queue:
{
"version": "1",
"default": "deny",
"hide": [
"purge-endpoint-queue"
]
}purge-endpoint-queue disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
Free to start. No card required.
Remove all pending jobs from a Serverless endpoint queue. Only affects queued jobs — in-progress jobs continue running. Use this for error recovery or clearing outdated requests. It is categorised as a Destructive tool in the RunPod MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the RunPod MCP Server MCP server in PolicyLayer and add a rule for purge-endpoint-queue: 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 RunPod MCP Server. Nothing to install.
purge-endpoint-queue 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 purge-endpoint-queue 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 purge-endpoint-queue. 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.
purge-endpoint-queue is provided by the RunPod MCP Server MCP server (runpod/runpod-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 36 RunPod MCP Server tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
36 RunPod MCP Server tools catalogued and risk-classified — across an index of 42,500+ MCP servers.