run_model

Test-mode fal mirror (sync placeholder).

SERVERLibi SOURCENagellabs/libi

High RISK CLASS

Category Execute

Parameters 00 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/libi/run-model.md

WHAT IT DOES

What run_model does on Libi

AI agents invoke run_model to trigger actions in Libi. 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.

RISK

Why run_model is rated High

Executes model inference with unpredictable effects depending on input arguments and model behavior.

From the tool's definition Test-mode fal mirror (sync placeholder) — runs external model inference.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs run_model safely

PolicyLayer is an MCP gateway: it sits between your AI agents and Libi, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For run_model, this is the rule to start with:

run_model Rate-limited

run_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": {
    "run_model": {
      "limits": [
        {
          "counter": "run_model_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}

RATE-LIMIT THIS TOOL → Instant setup, no code required.

The button opens the PolicyLayer dashboard: create your workspace, connect Libi, apply this rule, and every run_model call is checked against it from then on.

FAQ

Questions about run_model

What does the run_model tool do? +

Test-mode fal mirror (sync placeholder). It is categorised as a Execute tool in the Libi MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on run_model? +

Register the Libi MCP server in PolicyLayer and add a rule for run_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 Libi. Nothing to install.

What risk level is run_model? +

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

Can I rate-limit run_model? +

Yes. Add a rate_limit block to the run_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 run_model completely? +

Set action: deny in the PolicyLayer policy for run_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 run_model? +

run_model is provided by the Libi MCP server (Nagellabs/libi). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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