Focus the viewport on selected actors.
AI agents invoke focus_selected 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 drives a live editor action (viewport focus), making it an Execute category tool. It has minimal blast radius as it only changes the viewport camera position without modifying any data, hence low severity.
From the tool's definition 'Focus the viewport on selected actors' — triggers an editor viewport action in the live UEFN editor
Documented attack patterns abuse exactly the kind of access focus_selected 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 focus_selected:
{
"version": "1",
"default": "deny",
"tools": {
"focus_selected": {
"limits": [
{
"counter": "focus_selected_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}focus_selected 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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Focus the viewport on selected actors. 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 focus_selected: 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.
focus_selected 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 focus_selected 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 focus_selected. 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.
focus_selected 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.
Free to start. No card required.
143 Uefn tools catalogued and risk-classified — across an index of 43,000+ MCP servers.