Retrieves fine-tuning metrics if the job has succeeded.
AI agents call get_finetuning_metrics to retrieve information from Azure AI Foundry MCP Server without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Even though get_finetuning_metrics only reads data, uncontrolled read access leaks sensitive information and racks up API costs — an agent caught in a retry loop can make thousands of calls a minute without anyone noticing.
Attacks that exploit this kind of access
Retrieves fine-tuning metrics if the job has succeeded. It is categorised as a Read tool in the Azure AI Foundry MCP Server MCP Server, which means it retrieves data without modifying state.
Register the Azure AI Foundry 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 Foundry 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 Foundry MCP Server MCP server (youssef7788/mcp-foundry). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.