AI agents call delete_execution_logs to permanently remove resources in Kestra Python MCP Server — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
This tool performs a destructive operation by deleting execution logs. Deletion is irreversible and represents data loss. While the blast radius may be limited to logs rather than primary data, the permanent nature of the deletion places it in the Destructive category.
From the tool's definition Tool name is 'delete_execution_logs' which explicitly indicates permanent deletion of execution logs. The action is irreversible and cannot be undone.
Documented attack patterns abuse exactly the kind of access delete_execution_logs gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Kestra Python MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for delete_execution_logs:
{
"version": "1",
"default": "deny",
"hide": [
"delete_execution_logs"
]
}delete_execution_logs 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_execution_logs. It is categorised as a Destructive tool in the Kestra Python MCP Server MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Kestra Python MCP Server MCP server in PolicyLayer and add a rule for delete_execution_logs: 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 Kestra Python MCP Server. Nothing to install.
delete_execution_logs 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_execution_logs 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_execution_logs. 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_execution_logs is provided by the Kestra Python MCP Server MCP server (kestra-io/mcp-server-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Kestra Python MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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39 Kestra Python MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.