Select actors in the viewport.
AI agents invoke select_actors 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.
Selecting actors modifies the editor's active selection state and drives the live UEFN editor, which is an external operation affecting the editor environment. It is not a pure read (it changes state) nor destructive, so Execute is the most appropriate category.
From the tool's definition 'Select actors in the viewport' — triggers a UI/editor state change in a live UEFN editor session
Documented attack patterns abuse exactly the kind of access select_actors 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_actors:
{
"version": "1",
"default": "deny",
"tools": {
"select_actors": {
"limits": [
{
"counter": "select_actors_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}select_actors 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 actors in the viewport. 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_actors: 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_actors 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_actors 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_actors. 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_actors 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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