AI agents call delete_schedule to permanently remove resources in Prometheus MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
The 'delete' verb indicates an irreversible destructive action. Even without documentation, deletion of schedules in a Prometheus monitoring context could remove important data collection or alerting schedules, causing loss of observability and potential operational blind spots. This is more severe than Execute or Write because the action cannot be undone.
From the tool's definition Tool name is 'delete_schedule' which explicitly indicates deletion of a scheduled resource. The empty description prevents full verification of scope, but the verb 'delete' combined with the server context (AWS Managed Prometheus) strongly suggests…
Documented attack patterns abuse exactly the kind of access delete_schedule gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Prometheus MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_schedule:
{
"version": "1",
"default": "deny",
"hide": [
"delete_schedule"
]
}delete_schedule disappears from the agent's tool list entirely, and any attempt to call it is denied. The rest of the server keeps working.
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delete_schedule. It is categorised as a Destructive tool in the Prometheus MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Prometheus MCP Server MCP server in PolicyLayer and add a rule for delete_schedule: 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 Prometheus MCP Server. Nothing to install.
delete_schedule 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_schedule 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_schedule. 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_schedule is provided by the Prometheus MCP Server MCP server (awslabs.prometheus-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Prometheus MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
Free to start. No card required.
805 Prometheus MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.