Select all actors of a given class in the current level.
AI agents invoke select_by_class to trigger actions in Uefn. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool triggers an editor operation (selection) that affects the state of the live UEFN editor environment. While it doesn't delete or write persistent data, it executes an editor action that modifies the active selection state, which can have downstream effects (e.g., subsequent batch operations acting on the selection). It goes beyond a pure read/query operation because it changes editor state.
From the tool's definition Select all actors of a given class in the current level
Documented attack patterns abuse exactly the kind of access select_by_class gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Uefn, and nothing reaches the server without passing your rules. This is the rule we recommend for select_by_class:
{
"version": "1",
"default": "deny",
"tools": {
"select_by_class": {
"limits": [
{
"counter": "select_by_class_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}select_by_class stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Select all actors of a given class in the current level. It is categorised as a Execute tool in the Uefn MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Uefn MCP server in PolicyLayer and add a rule for select_by_class: 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 Uefn. Nothing to install.
select_by_class is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the select_by_class 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 select_by_class. 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.
select_by_class is provided by the Uefn MCP server (quangdang46/uefn-verse-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Uefn, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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