rhino_bridge_select_instance
A execute tool on the Rhino MCP server.
This record as markdown: /tools/easehee-rhino-mcp/rhino-bridge-select-instance.md
What rhino_bridge_select_instance does on Rhino
AI agents invoke rhino_bridge_select_instance to trigger actions in Rhino. 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.
Why rhino_bridge_select_instance is rated High
The description is empty, making classification uncertain. Based on the name, 'bridge_select_instance' likely interacts with the Rhino application to select or target a running instance, which would be an Execute-level operation (triggering external application state changes). Confidence is low due to lack of description. Could also be Read (just querying/selecting an instance without side effects).
From the tool's definition Tool name 'rhino_bridge_select_instance' with empty description; 'select_instance' suggests selecting/activating a Rhino bridge instance, which likely triggers an external operation in Rhino.
Attacks that exploit this kind of access
The rule that runs rhino_bridge_select_instance safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Rhino, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For rhino_bridge_select_instance, this is the rule to start with:
rhino_bridge_select_instance 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.
The button opens the PolicyLayer dashboard: create your workspace, connect Rhino, apply this rule, and every rhino_bridge_select_instance call is checked against it from then on.
Questions about rhino_bridge_select_instance
rhino_bridge_select_instance is a execute tool on the Rhino MCP server. It is categorised as a Execute tool in the Rhino MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Rhino MCP server in PolicyLayer and add a rule for rhino_bridge_select_instance: 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 Rhino. Nothing to install.
rhino_bridge_select_instance 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 rhino_bridge_select_instance 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 rhino_bridge_select_instance. 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.
rhino_bridge_select_instance is provided by the Rhino MCP server (easehee/rhino-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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