Retrieves fine-tuning metrics if the job has succeeded. Calls fetch_finetuning_status to confirm job completion. Then fetches the result.csv content using the result_file_id.
AI agents call get_finetuning_metrics 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.
This tool only queries and retrieves data about fine-tuning job metrics and results. It performs no side effects, creates no data, executes no code, and deletes nothing. It is purely informational, making it a Read operation with low severity. The confidence is high because the description clearly indicates retrieval-only operations (fetches, retrieves).
From the tool's definition Tool description states it 'Retrieves fine-tuning metrics' and 'fetches the result.csv content'. Both are retrieval operations with no modification, deletion, or code execution.
Documented attack patterns abuse exactly the kind of access get_finetuning_metrics gives an agent:
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 get_finetuning_metrics:
{
"version": "1",
"default": "deny",
"tools": {
"get_finetuning_metrics": {}
}
}get_finetuning_metrics is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Retrieves fine-tuning metrics if the job has succeeded. Calls fetch_finetuning_status to confirm job completion. Then fetches the result.csv content using the result_file_id. 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.
Register the Azure AI Agent Service MCP Server MCP server in PolicyLayer and add a rule for get_finetuning_metrics: 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.
get_finetuning_metrics 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 get_finetuning_metrics 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 get_finetuning_metrics. 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.
get_finetuning_metrics 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.
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.