Delete a resource/attachment from Joplin. WARNING: This will break references in notes that use this resource. Use get_resource_notes first to check usage.
Part of the Pypi:joplin server.
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AI agents may call delete_resource to permanently remove or destroy resources in Pypi:joplin. 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_resource in a loop, permanently destroying resources in Pypi:joplin. 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_resource"
]
}See the full Pypi:joplin policy for all 32 tools.
These attack patterns abuse exactly the kind of access delete_resource 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 resource/attachment from Joplin. WARNING: This will break references in notes that use this resource. Use get_resource_notes first to check usage.. It is categorised as a Destructive tool in the Pypi:joplin MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Pypi:joplin MCP server in PolicyLayer and add a rule for delete_resource: 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 Pypi:joplin. Nothing to install.
delete_resource 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_resource 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_resource. 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_resource is provided by the Pypi:joplin MCP server (pypi:joplin-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 32 Pypi:joplin tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
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