schedule_job | PolicyLayer

WHAT IT DOES

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

AI agents invoke schedule_job 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 schedule_job needs a policy

This tool triggers periodic execution of jobs in a ML platform. While it doesn't run code directly in this invocation, it configures automated recurring job execution, which constitutes triggering external operations. Misuse could cause unintended repeated job runs, resource exhaustion, or unintended ML workloads being executed on a schedule.

From the tool's definition 'Schedule a job to run periodically using a cron expression' — schedules recurring execution of jobs

Attacks that exploit this kind of access

Recommended rule Ready

Verdict

schedule_job Rate-limited

schedule_job 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": {
    "schedule_job": {
      "limits": [
        {
          "counter": "schedule_job_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 schedule_job

What does the schedule_job tool do? +

Schedule a job to run periodically using a cron expression. 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 schedule_job? +

Register the Cloudera Machine Learning (CML) MCP Server MCP server in PolicyLayer and add a rule for schedule_job: 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 schedule_job? +

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

Can I rate-limit schedule_job? +

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

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

schedule_job 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.