Query an agent and evaluate its response in a single operation. Parameters: - agent_id: ID of the agent to query - query: Text query to send to the agent - evaluator_names: Optional list of agent evaluator names to use (defaults to all) - include_studio_url: Whether to include the Azure AI studio...
AI agents invoke agent_query_and_evaluate to trigger actions in Azure AI Agent Service MCP Server. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool triggers external operations by sending queries to an AI agent and running evaluations against it. It interacts with live Azure AI infrastructure (agents, evaluators) and produces side effects by initiating agent inference and evaluation pipelines. Since it combines querying with executing evaluations, it falls under Execute.
From the tool's definition Query an agent and evaluate its response in a single operation
Documented attack patterns abuse exactly the kind of access agent_query_and_evaluate 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 agent_query_and_evaluate:
{
"version": "1",
"default": "deny",
"tools": {
"agent_query_and_evaluate": {
"limits": [
{
"counter": "agent_query_and_evaluate_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}agent_query_and_evaluate stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
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Query an agent and evaluate its response in a single operation. Parameters: - agent_id: ID of the agent to query - query: Text query to send to the agent - evaluator_names: Optional list of agent evaluator names to use (defaults to all) - include_studio_url: Whether to include the Azure AI studio URL in the response Returns both the agent response and evaluation results. It is categorised as a Execute tool in the Azure AI Agent Service MCP Server MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Azure AI Agent Service MCP Server MCP server in PolicyLayer and add a rule for agent_query_and_evaluate: 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.
agent_query_and_evaluate is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the agent_query_and_evaluate 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 agent_query_and_evaluate. 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.
agent_query_and_evaluate 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.