Cancel multiple orders in one request.
AI agents call aevo_cancel_orders to permanently remove resources in Aevo-MCP — typically in cleanup and lifecycle workflows. It does its job in a single call, and there is no undo.
Cancelling multiple orders is irreversible in the context of trading — once cancelled, those order positions in the market are gone and cannot be restored. This action can have significant financial consequences if an AI agent mistakenly cancels active orders, making it Destructive with high severity.
From the tool's definition Cancel multiple orders in one request
Attacks that exploit this kind of access
Cancel multiple orders in one request. It is categorised as a Destructive tool in the Aevo-MCP MCP Server, which means it can permanently delete or destroy data. Block by default and require explicit approval.
Register the Aevo- MCP server in PolicyLayer and add a rule for aevo_cancel_orders: 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 Aevo-MCP. Nothing to install.
aevo_cancel_orders 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 aevo_cancel_orders 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 aevo_cancel_orders. 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.
aevo_cancel_orders is provided by the Aevo- MCP server (ribbon-finance/aevo-mcp). 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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