list_finetuning_jobs

THE RISK

Low Risk

Why list_finetuning_jobs needs a policy

This tool retrieves and returns metadata about existing finetuning jobs without modifying, deleting, or executing any operations. Listing jobs is a safe read operation that has minimal blast radius if misused—an agent could at worst enumerate jobs to discover information already accessible through the Azure account.

From the tool's definition Tool description states it 'list[s] all finetuning jobs' and 'Returns: List of dictionaries containing job ID and status.' The verb 'list' combined with returning read-only information (job IDs and statuses) indicates a query operation with no side effects.

Documented attack patterns abuse exactly the kind of access list_finetuning_jobs gives an agent:

POLICY

How to control list_finetuning_jobs

PolicyLayer is an MCP gateway — it sits between your AI agents and Azure AI Agent Service MCP Server, and nothing reaches the server without passing your rules. This is the rule we recommend for list_finetuning_jobs:

policy.json

{
  "version": "1",
  "default": "deny",
  "tools": {
    "list_finetuning_jobs": {}
  }
}

list_finetuning_jobs is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.

Create a free account and register Azure AI Agent Service MCP Server — nothing to install.
Add this policy — paste it, or build it visually.
Point your MCP client (Claude, Cursor, anything) at your gateway URL.

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EXPLORE

Related tools and policies

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Go deeper

The MCP Attack Database → Documented attack patterns against MCP deployments — and the policies that stop them.
Data exfiltration → How read access becomes an exfiltration channel when chained with untrusted content.
MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
MCP Security: Why Prompt Guardrails Aren't Enough →

FAQ

Questions about list_finetuning_jobs

What does the list_finetuning_jobs tool do? +

MCP-compatible function to list all finetuning jobs using Azure OpenAI API. Returns: List of dictionaries containing job ID and status. It is categorised as a Read tool in the Azure AI Agent Service MCP Server MCP Server, which means it retrieves data without modifying state.

How do I enforce a policy on list_finetuning_jobs? +

Register the Azure AI Agent Service MCP Server MCP server in PolicyLayer and add a rule for list_finetuning_jobs: 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 Azure AI Agent Service MCP Server. Nothing to install.

What risk level is list_finetuning_jobs? +

list_finetuning_jobs is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit list_finetuning_jobs? +

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

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

list_finetuning_jobs is provided by the Azure AI Agent Service MCP Server MCP server (microsoft-foundry/mcp-foundry). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.

Enforce policy on every Azure AI Agent Service MCP Server tool call.

Start from Azure AI Agent Service MCP Server, add the rest of your stack, and see everything your agents can call. Then put policy on all of it.

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