Delete a learning by key. Use when a learning is outdated or incorrect.
Part of the Nodebench server.
Free to start. No card required.
AI agents may call delete_learning to permanently remove or destroy resources in Nodebench. Without a policy, an autonomous agent could delete critical data in a loop with no way to undo the damage. PolicyLayer blocks destructive tools by default and requires explicit human approval before enabling them.
Without a policy, an AI agent could call delete_learning in a loop, permanently destroying resources in Nodebench. There is no undo for destructive operations. PolicyLayer blocks this tool by default and only allows it when a human explicitly approves the action.
Destructive tools permanently remove data. Block by default. Only enable with explicit approval workflows.
{
"version": "1",
"default": "deny",
"hide": [
"delete_learning"
]
}See the full Nodebench policy for all 724 tools.
These attack patterns abuse exactly the kind of access delete_learning gives an agent. Each links to the full case and the policy that stops it:
Other destructive tools across the catalogue. The same approach applies to each: deny by default, or require human approval.
Delete a learning by key. Use when a learning is outdated or incorrect.. It is categorised as a Destructive tool in the Nodebench MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Nodebench MCP server in PolicyLayer and add a rule for delete_learning: 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 Nodebench. Nothing to install.
delete_learning 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_learning 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_learning. 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_learning is provided by the Nodebench MCP server (nodebench-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 724 Nodebench tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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