AI agents call job_status to retrieve information from LLM CLI Gateway without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves the status of an existing job, which is a read-only operation with no side effects. The term 'Check' combined with the passive retrieval of job status places it squarely in the Read category. Low severity because status checks pose minimal risk even if misused—they cannot alter system state or trigger unwanted operations.
From the tool's definition Tool name 'job_status' and description 'Check a VALIDATION job' indicate a query operation that retrieves status information without modifying, executing, or deleting data.
Documented attack patterns abuse exactly the kind of access job_status gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and LLM CLI Gateway, and nothing reaches the server without passing your rules. This is the rule we recommend for job_status:
{
"version": "1",
"default": "deny",
"tools": {
"job_status": {}
}
}job_status is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Check a VALIDATION job. It is categorised as a Read tool in the LLM CLI Gateway MCP Server, which means it retrieves data without modifying state.
Register the LLM CLI Gateway MCP server in PolicyLayer and add a rule for job_status: 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 LLM CLI Gateway. Nothing to install.
job_status is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the job_status 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.
Set action: deny in the PolicyLayer policy for job_status. 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.
job_status is provided by the LLM CLI Gateway MCP server (llm-cli-gateway). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Start from LLM CLI Gateway, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.
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46 LLM CLI Gateway tools catalogued and risk-classified — across an index of 43,000+ MCP servers.