Get the required input fields for a specific agent evaluator or all agent evaluators. Parameters: - evaluator_name: Optional name of evaluator. If None, returns requirements for all evaluators.
AI agents call get_agent_evaluator_requirements 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 retrieves metadata about evaluator requirements without modifying any state, executing code, or affecting resources. It has no side effects and serves only to inform the user about available evaluator configurations.
From the tool's definition Tool description states 'Get the required input fields' which is a retrieval operation. The verb 'Get' and the lack of any modification, execution, deletion, or financial operation confirms this is a pure query function.
Documented attack patterns abuse exactly the kind of access get_agent_evaluator_requirements 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_agent_evaluator_requirements:
{
"version": "1",
"default": "deny",
"tools": {
"get_agent_evaluator_requirements": {}
}
}get_agent_evaluator_requirements is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Get the required input fields for a specific agent evaluator or all agent evaluators. Parameters: - evaluator_name: Optional name of evaluator. If None, returns requirements for all evaluators. 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_agent_evaluator_requirements: 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_agent_evaluator_requirements 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_agent_evaluator_requirements 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_agent_evaluator_requirements. 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_agent_evaluator_requirements 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.