codex_fix_code · ML Lab MCP: Risk & Policy

WHAT IT DOES

What codex_fix_code does on ML Lab MCP

AI agents invoke codex_fix_code to trigger actions in ML Lab MCP. 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 codex_fix_code needs a policy

This tool doesn't just suggest fixes; it applies them to training/evaluation code. Modifying code that runs on GPU clusters or cloud providers (Lambda Labs, RunPod, SSH VPS) can have significant side effects including altering model training behavior, introducing bugs, or changing evaluation logic.

From the tool's definition 'Have Codex fix issues in training or evaluation code' — actively modifies and executes code fixes in the ML engineering environment

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

codex_fix_code Rate-limited

codex_fix_code 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": {
    "codex_fix_code": {
      "limits": [
        {
          "counter": "codex_fix_code_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 ML Lab MCP, so you can run agents against it safely.

FAQ

Questions about codex_fix_code

What does the codex_fix_code tool do? +

Have Codex fix issues in training or evaluation code. It is categorised as a Execute tool in the ML Lab MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on codex_fix_code? +

Register the ML Lab MCP server in PolicyLayer and add a rule for codex_fix_code: 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 ML Lab MCP. Nothing to install.

What risk level is codex_fix_code? +

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

Can I rate-limit codex_fix_code? +

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

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

codex_fix_code is provided by the ML Lab MCP server (pushpullcommitpush/ml-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.