Destructive operations that remove data. Supported operations: delete_element, clear, clear_github_auth Element types: persona, skill, template, agent, memory, ensemble These operations remove data. Use with caution. ⚠️ SECURITY: Do not auto-allow this endpoint in your host settings (e.g., Claude...
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AI agents may call mcp_aql_delete to permanently remove or destroy resources in DollhouseMCP. 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 mcp_aql_delete in a loop, permanently destroying resources in DollhouseMCP. 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": [
"mcp_aql_delete"
]
}See the full DollhouseMCP policy for all 5 tools.
These attack patterns abuse exactly the kind of access mcp_aql_delete 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.
Destructive operations that remove data. Supported operations: delete_element, clear, clear_github_auth Element types: persona, skill, template, agent, memory, ensemble These operations remove data. Use with caution. ⚠️ SECURITY: Do not auto-allow this endpoint in your host settings (e.g., Claude Code settings.json). Each delete operation should require explicit human approval. Auto-allowing bypasses the per-operation confirmation gate, leaving only element deny policies as protection against unintended data loss. Quick start examples: { operation: "delete_element", element_type: "persona", params: { element_name: "Old-Persona" } } { operation: "clear", params: { element_name: "temp-notes" } } { operation: "clear_github_auth" } Discover required parameters — use mcp_aql_read: { operation: "introspect", params: { query: "operations", name: "delete_element" } }. It is categorised as a Destructive tool in the DollhouseMCP MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Dollhouse MCP server in PolicyLayer and add a rule for mcp_aql_delete: 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 DollhouseMCP. Nothing to install.
mcp_aql_delete 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 mcp_aql_delete 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 mcp_aql_delete. 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.
mcp_aql_delete is provided by the Dollhouse MCP server (@dollhousemcp/mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 5 DollhouseMCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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