run_playtest_scenario · Melo MCP: Risk & Policy

WHAT IT DOES

What run_playtest_scenario does on Melo

AI agents invoke run_playtest_scenario to trigger actions in Melo. 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 run_playtest_scenario needs a policy

This tool executes a sequence of commands (wait, key actions, etc.) that trigger external effects in the Roblox game environment. While not destructive or financial, it actively orchestrates and runs operations whose effects depend on the playtest scenario arguments. The ability to control character actions, timing, and assertions makes this an Execute category tool.

From the tool's definition Tool 'orchestrates a sequence of playtest steps' which involves executing sequential operations within Roblox Studio, including key input actions and wait operations that trigger game behavior.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

run_playtest_scenario Rate-limited

run_playtest_scenario 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_playtest_scenario": {
      "limits": [
        {
          "counter": "run_playtest_scenario_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 Melo, so you can run agents against it safely.

FAQ

Questions about run_playtest_scenario

What does the run_playtest_scenario tool do? +

Orchestrate a sequence of playtest steps and return a structured verdict. Steps run in order; the run aborts on the first failing assertion or unhandled error. Always calls stop_playtest at the end, even on failure. Step kinds: - wait { seconds } - key { key, action (. It is categorised as a Execute tool in the Melo 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_playtest_scenario? +

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

What risk level is run_playtest_scenario? +

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

Can I rate-limit run_playtest_scenario? +

Yes. Add a rate_limit block to the run_playtest_scenario 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_playtest_scenario completely? +

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

run_playtest_scenario is provided by the Melo MCP server (yannyhl/linkedsword-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.