viewer_fit · PyNet Bridge MCP: Risk & Policy

WHAT IT DOES

What viewer_fit does on PyNet Bridge

AI agents invoke viewer_fit to trigger actions in PyNet Bridge. 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 viewer_fit needs a policy

This tool triggers an external operation (adjusting the camera view in a viewer application). It has no data creation/deletion implications, but it does execute an action in an external UI/viewer context. The blast radius is low as it only affects the visual camera position.

From the tool's definition Fits the camera to all models in the open viewer

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

viewer_fit Rate-limited

viewer_fit 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": {
    "viewer_fit": {
      "limits": [
        {
          "counter": "viewer_fit_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 PyNet Bridge, so you can run agents against it safely.

FAQ

Questions about viewer_fit

What does the viewer_fit tool do? +

Fits the camera to all models in the open viewer. It is categorised as a Execute tool in the PyNet Bridge MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on viewer_fit? +

Register the PyNet Bridge MCP server in PolicyLayer and add a rule for viewer_fit: 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 PyNet Bridge. Nothing to install.

What risk level is viewer_fit? +

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

Can I rate-limit viewer_fit? +

Yes. Add a rate_limit block to the viewer_fit 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 viewer_fit completely? +

Set action: deny in the PolicyLayer policy for viewer_fit. 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 viewer_fit? +

viewer_fit is provided by the PyNet Bridge MCP server (rafael-nunezdearenas/pynetbridge). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.