fetch_finetuning_status

Fetches the status of a fine-tuning job using Azure OpenAI API. Parameters: - job_id: The ID of the fine-tuning job. Returns: - Job status information as a JSON string.

ServerAzure AI Agent Service MCP Server
CategoryRead

How to control fetch_finetuning_status ↓

WHAT IT DOES

What fetch_finetuning_status does on Azure AI Agent Service MCP Server

AI agents call fetch_finetuning_status to retrieve information from Azure AI Agent Service MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.

THE RISK

Low Risk

Why fetch_finetuning_status needs a policy

This tool retrieves status information about an existing fine-tuning job without creating, modifying, deleting, or executing any operations. It is a simple query operation that reads job metadata from Azure OpenAI API. No financial transactions, destructive actions, or code execution are involved.

From the tool's definition Tool name is 'fetch_finetuning_status' and description states it 'Fetches the status of a fine-tuning job' and 'Returns: Job status information as a JSON string.' The verb 'fetch' and 'returns' indicate data retrieval with no modification or side effects.

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

POLICY

How to control fetch_finetuning_status

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 fetch_finetuning_status:

policy.json

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

fetch_finetuning_status 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.
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MCP token cost → What connecting this server costs in context-window tokens on every request.
Rate Limiting MCP Tool Calls: A Practical Guide →
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FAQ

Questions about fetch_finetuning_status

What does the fetch_finetuning_status tool do? +

Fetches the status of a fine-tuning job using Azure OpenAI API. Parameters: - job_id: The ID of the fine-tuning job. Returns: - Job status information as a JSON string. 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 fetch_finetuning_status? +

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

What risk level is fetch_finetuning_status? +

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

Can I rate-limit fetch_finetuning_status? +

Yes. Add a rate_limit block to the fetch_finetuning_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.

How do I block fetch_finetuning_status completely? +

Set action: deny in the PolicyLayer policy for fetch_finetuning_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.

What MCP server provides fetch_finetuning_status? +

fetch_finetuning_status 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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28 Azure AI Agent Service MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.