Delete an annotation queue.
AI agents call delete_annotation_queue to permanently remove resources in Langfuse Mcp Python — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
The tool permanently deletes an annotation queue, which cannot be undone. This is a destructive operation that removes existing data structures and any associated metadata. While not directly affecting production systems outside the monitoring context, loss of annotation queues could disrupt workflows and audit trails. High severity due to irreversibility; confidence is high because the intent is unambiguous.
From the tool's definition Tool name includes 'delete' and description states 'Delete an annotation queue' — irreversible removal of data.
Attacks that exploit this kind of access
Delete an annotation queue. It is categorised as a Destructive tool in the Langfuse Mcp Python MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Langfuse Mcp Python MCP server in PolicyLayer and add a rule for delete_annotation_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 Langfuse Mcp Python. Nothing to install.
delete_annotation_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 delete_annotation_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 delete_annotation_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.
delete_annotation_queue is provided by the Langfuse Mcp Python MCP server (log-logn/langfuse-mcp-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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