rig_model · Meshy Bottube MCP: Risk & Policy

WHAT IT DOES

What rig_model does on Meshy Bottube

AI agents invoke rig_model to trigger actions in Meshy Bottube. 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 IT NEEDS A POLICY

Why rig_model needs a policy

This tool executes an automated external process (auto-rigging) that applies a skeleton to a 3D model. It doesn't simply read data, nor does it delete or move money. It triggers an operation whose effect depends on the input model, placing it in the Execute category. Severity is medium as misuse could corrupt or incorrectly rig a model asset, though it is not broadly destructive.

From the tool's definition 'Auto-rig a humanoid model for animation (a skeleton)' — triggers an external automated rigging operation on a model asset

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

rig_model Rate-limited

rig_model 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.

View as policy code

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "rig_model": {
      "limits": [
        {
          "counter": "rig_model_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL →

Instant setup, no code required.

PolicyLayer is an MCP gateway: every tool call is checked against your rule before it reaches Meshy Bottube, so you can run agents against it safely.

FAQ

Questions about rig_model

What does the rig_model tool do? +

Auto-rig a humanoid model for animation (a skeleton). Identify it by. It is categorised as a Execute tool in the Meshy Bottube MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on rig_model? +

Register the Meshy Bottube MCP server in PolicyLayer and add a rule for rig_model: 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 Meshy Bottube. Nothing to install.

What risk level is rig_model? +

rig_model is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.

Can I rate-limit rig_model? +

Yes. Add a rate_limit block to the rig_model 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.

How do I block rig_model completely? +

Set action: deny in the PolicyLayer policy for rig_model. 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.

What MCP server provides rig_model? +

rig_model is provided by the Meshy Bottube MCP server (scottcjn/meshy-bottube-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.