run_gauntlet

Launch a Gauntlet test session via UAT. Runs tests in a full game instance.

SERVERUnreal SOURCE@jjpxdev/unreal-mcp

High RISK CLASS

Category Execute

Parameters 31 required

Recommended Rate-limitedsee the rule below

Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/jjpxdev-unreal-mcp/run-gauntlet.md

WHAT IT DOES

What run_gauntlet does on Unreal

AI agents invoke run_gauntlet to trigger actions in Unreal. 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.

Parameter	Type	Required	Description
`platform`	object	—	Target platform
`test_name`	string	Yes	Gauntlet test name
`configuration`	object	—	Build configuration

Parameters from the server's own tool schema.

RISK

Why run_gauntlet is rated High

The tool triggers execution of arbitrary test suites (Gauntlet tests) within a live Unreal Engine game instance via the Unreal Automation Tool (UAT). This is a code execution action whose effects depend entirely on what tests are specified—tests could interact with game state, modify assets, access sensitive data, or cause unpredictable side effects.

From the tool's definition Tool description explicitly states 'Launch a Gauntlet test session via UAT' and 'Runs tests in a full game instance.' These clearly indicate execution of test code within a game runtime environment.

Attacks that exploit this kind of access

RECOMMENDED RULE

The rule that runs run_gauntlet safely

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

run_gauntlet Rate-limited

run_gauntlet 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_gauntlet": {
      "limits": [
        {
          "counter": "run_gauntlet_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 Unreal, apply this rule, and every run_gauntlet call is checked against it from then on.

FAQ

Questions about run_gauntlet

What does the run_gauntlet tool do? +

Launch a Gauntlet test session via UAT. Runs tests in a full game instance. It is categorised as a Execute tool in the Unreal MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

What parameters does run_gauntlet accept? +

run_gauntlet accepts 3 parameters: platform, test_name, configuration. Required: test_name. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on run_gauntlet? +

Register the Unreal MCP server in PolicyLayer and add a rule for run_gauntlet: 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 Unreal. Nothing to install.

What risk level is run_gauntlet? +

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

Can I rate-limit run_gauntlet? +

Yes. Add a rate_limit block to the run_gauntlet 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_gauntlet completely? +

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

run_gauntlet is provided by the Unreal MCP server (@jjpxdev/unreal-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

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