Returns a list of available agent evaluator names for evaluating agent behaviors.
AI agents call list_agent_evaluators 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 queries and retrieves metadata about available agent evaluators. It performs no mutation, deletion, code execution, or financial operation. The action is purely informational—fetching a catalog of evaluator names for the user to review. This is a classic Read operation with minimal security risk; an agent calling this tool cannot cause harm beyond accessing non-sensitive enumerative data.
From the tool's definition Tool 'list_agent_evaluators' 'returns a list' of available evaluators; the word 'returns' and 'list' indicate a read-only retrieval operation with no side effects.
Documented attack patterns abuse exactly the kind of access list_agent_evaluators 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 list_agent_evaluators:
{
"version": "1",
"default": "deny",
"tools": {
"list_agent_evaluators": {}
}
}list_agent_evaluators is read-only, so it stays allowed — but everything else on the server is denied unless you say otherwise.
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Returns a list of available agent evaluator names for evaluating agent behaviors. 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 list_agent_evaluators: 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.
list_agent_evaluators 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 list_agent_evaluators 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 list_agent_evaluators. 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.
list_agent_evaluators 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.
Free to start. No card required.
28 Azure AI Agent Service MCP Server tools catalogued and risk-classified — across an index of 43,000+ MCP servers.