chore: import upstream snapshot with attribution

2026-07-13 13:39:25 +08:00
commit db620d33df
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+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample shows how to create and use a simple AI agent with OpenAI Responses as the backend, that uses a Hosted MCP Tool.
+// In this case the OpenAI responses service will invoke any MCP tools as required. MCP tools are not invoked by the Agent Framework.
+// The sample first shows how to use MCP tools with auto approval, and then how to set up a tool that requires approval before it can be invoked and how to approve such a tool.
+
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Extensions.AI;
+using OpenAI.Responses;
+
+var endpoint = Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT") ?? throw new InvalidOperationException("AZURE_OPENAI_ENDPOINT is not set.");
+var deploymentName = Environment.GetEnvironmentVariable("AZURE_OPENAI_DEPLOYMENT_NAME") ?? "gpt-5.4-mini";
+
+// **** MCP Tool with Auto Approval ****
+// *************************************
+
+// Create an MCP tool definition that the agent can use.
+// In this case we allow the tool to always be called without approval.
+var mcpTool = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
+{
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.NeverRequire
+};
+
+// Create an agent based on Azure OpenAI Responses as the backend.
+// WARNING: DefaultAzureCredential is convenient for development but requires careful consideration in production.
+// In production, consider using a specific credential (e.g., ManagedIdentityCredential) to avoid
+// latency issues, unintended credential probing, and potential security risks from fallback mechanisms.
+AIAgent agent = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+     .GetResponsesClient()
+     .AsAIAgent(
+        model: deploymentName,
+        instructions: "You answer questions by searching the Microsoft Learn content only.",
+        name: "MicrosoftLearnAgent",
+        tools: [mcpTool]);
+
+// You can then invoke the agent like any other AIAgent.
+AgentSession session = await agent.CreateSessionAsync();
+Console.WriteLine(await agent.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", session));
+
+// **** MCP Tool with Approval Required ****
+// *****************************************
+
+// Create an MCP tool definition that the agent can use.
+// In this case we require approval before the tool can be called.
+var mcpToolWithApproval = new HostedMcpServerTool(
+    serverName: "microsoft_learn",
+    serverAddress: "https://learn.microsoft.com/api/mcp")
+{
+    AllowedTools = ["microsoft_docs_search"],
+    ApprovalMode = HostedMcpServerToolApprovalMode.AlwaysRequire
+};
+
+// Create an agent based on Azure OpenAI Responses as the backend.
+AIAgent agentWithRequiredApproval = new AzureOpenAIClient(
+    new Uri(endpoint),
+    new DefaultAzureCredential())
+    .GetResponsesClient()
+    .AsAIAgent(
+        model: deploymentName,
+        instructions: "You answer questions by searching the Microsoft Learn content only.",
+        name: "MicrosoftLearnAgentWithApproval",
+        tools: [mcpToolWithApproval]);
+
+// You can then invoke the agent like any other AIAgent.
+// For simplicity, we are assuming here that only mcp tool approvals are pending.
+AgentSession sessionWithRequiredApproval = await agentWithRequiredApproval.CreateSessionAsync();
+AgentResponse response = await agentWithRequiredApproval.RunAsync("Please summarize the Azure AI Agent documentation related to MCP Tool calling?", sessionWithRequiredApproval);
+List<ToolApprovalRequestContent> approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+
+while (approvalRequests.Count > 0)
+{
+    // Ask the user to approve each MCP call request.
+    List<ChatMessage> userInputResponses = approvalRequests
+        .ConvertAll(approvalRequest =>
+        {
+            McpServerToolCallContent mcpToolCall = (McpServerToolCallContent)approvalRequest.ToolCall!;
+            Console.WriteLine($"""
+                The agent would like to invoke the following MCP Tool, please reply Y to approve.
+                ServerName: {mcpToolCall.ServerName}
+                Name: {mcpToolCall.Name}
+                Arguments: {string.Join(", ", mcpToolCall.Arguments?.Select(x => $"{x.Key}: {x.Value}") ?? [])}
+                """);
+            return new ChatMessage(ChatRole.User, [approvalRequest.CreateResponse(Console.ReadLine()?.Equals("Y", StringComparison.OrdinalIgnoreCase) ?? false)]);
+        });
+
+    // Pass the user input responses back to the agent for further processing.
+    response = await agentWithRequiredApproval.RunAsync(userInputResponses, sessionWithRequiredApproval);
+
+    approvalRequests = response.Messages.SelectMany(m => m.Contents).OfType<ToolApprovalRequestContent>().ToList();
+}
+
+Console.WriteLine($"\nAgent: {response}");