codex_debug_training · ML Lab MCP: Risk & Policy

WHAT IT DOES

What codex_debug_training does on ML Lab MCP

AI agents invoke codex_debug_training 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_debug_training needs a policy

Debugging training issues typically involves running diagnostic code, modifying scripts, or executing commands against an ML training environment. Given the sibling tools (codex_run, codex_fix_code, codex_generate_training_script), 'debug' likely involves active execution steps beyond passive reading.

From the tool's definition 'debug training issues from logs' — Codex actively analyzes and likely executes or modifies code/configs to resolve training issues, not merely reading logs passively

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

codex_debug_training Rate-limited

codex_debug_training 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_debug_training": {
      "limits": [
        {
          "counter": "codex_debug_training_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_debug_training

What does the codex_debug_training tool do? +

Have Codex debug training issues from logs. 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_debug_training? +

Register the ML Lab MCP server in PolicyLayer and add a rule for codex_debug_training: 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_debug_training? +

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

Can I rate-limit codex_debug_training? +

Yes. Add a rate_limit block to the codex_debug_training 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_debug_training completely? +

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

codex_debug_training 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.