Select a game object in the Unity Editor.
AI agents invoke select_object to trigger actions in Unity MCP with Ollama Integration. 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 an object in the Unity Editor triggers an editor operation/action rather than a passive read. It changes the editor's selection state, which can have side effects (e.g., triggering Inspector updates, selection-based scripts, or workflows). However, it does not modify asset data or delete anything, so the blast radius is low.
From the tool's definition Select a game object in the Unity Editor
Documented attack patterns abuse exactly the kind of access select_object gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and Unity MCP with Ollama Integration, and nothing reaches the server without passing your rules. This is the rule we recommend for select_object:
{
"version": "1",
"default": "deny",
"tools": {
"select_object": {
"limits": [
{
"counter": "select_object_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}select_object 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 a game object in the Unity Editor. It is categorised as a Execute tool in the Unity MCP with Ollama Integration MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Unity MCP with Ollama Integration MCP server in PolicyLayer and add a rule for select_object: 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 Unity MCP with Ollama Integration. Nothing to install.
select_object 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_object 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_object. 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_object is provided by the Unity MCP with Ollama Integration MCP server (zundamonnovrchatkaisetu/unity-mcp-ollama). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from Unity MCP with Ollama Integration, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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37 Unity MCP with Ollama Integration tools catalogued and risk-classified — across an index of 43,000+ MCP servers.