stop_job_run | PolicyLayer

WHAT IT DOES

What stop_job_run does on Cloudera Machine Learning (CML) MCP Server

AI agents invoke stop_job_run to trigger actions in Cloudera Machine Learning (CML) MCP Server. 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 stop_job_run needs a policy

The tool triggers an operation (job termination) with real-world effects that depend on which job run is targeted. While not destructive in the data-deletion sense, it halts an active computational process, which qualifies as Execute rather than Write. The severity is high because stopping a critical job run could disrupt important ML workflows, model training, or data processing pipelines.

From the tool's definition Tool description states 'Stop a running job in a CML project' - this is an action that interrupts an executing process, which is a form of execution control.

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

stop_job_run Rate-limited

stop_job_run 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": {
    "stop_job_run": {
      "limits": [
        {
          "counter": "stop_job_run_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 Cloudera Machine Learning (CML) MCP Server, so you can run agents against it safely.

FAQ

Questions about stop_job_run

What does the stop_job_run tool do? +

Stop a running job in a CML project. It is categorised as a Execute tool in the Cloudera Machine Learning (CML) MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.

How do I enforce a policy on stop_job_run? +

Register the Cloudera Machine Learning (CML) MCP Server MCP server in PolicyLayer and add a rule for stop_job_run: 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 Cloudera Machine Learning (CML) MCP Server. Nothing to install.

What risk level is stop_job_run? +

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

Can I rate-limit stop_job_run? +

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

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

stop_job_run is provided by the Cloudera Machine Learning (CML) MCP Server MCP server (yw449/cloudera-cml-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.