chore: import upstream snapshot with attribution

dotnet/samples/02-agents/ModelContextProtocol/Agent_MCP_LongRunningTask_Client/Agent_MCP_LongRunningTask_Client.csproj

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+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <OutputType>Exe</OutputType>
+    <TargetFrameworks>net10.0</TargetFrameworks>
+
+    <Nullable>enable</Nullable>
+    <ImplicitUsings>enable</ImplicitUsings>
+    <NoWarn>$(NoWarn);MAAI001;MEAI001;MCPEXP001</NoWarn>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Azure.AI.OpenAI" />
+    <PackageReference Include="Azure.Identity" />
+    <PackageReference Include="Microsoft.Extensions.AI.OpenAI" />
+    <PackageReference Include="Microsoft.Extensions.Hosting" />
+    <PackageReference Include="ModelContextProtocol" />
+  </ItemGroup>
+
+  <ItemGroup>
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.Mcp\Microsoft.Agents.AI.Mcp.csproj" />
+    <ProjectReference Include="..\..\..\..\src\Microsoft.Agents.AI.OpenAI\Microsoft.Agents.AI.OpenAI.csproj" />
+  </ItemGroup>
+
+</Project>

dotnet/samples/02-agents/ModelContextProtocol/Agent_MCP_LongRunningTask_Client/Program.cs

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+// Copyright (c) Microsoft. All rights reserved.
+
+// This sample demonstrates the Microsoft Agent Framework's MCP long-running task support.
+//
+// A small MCP server (hosted in this same executable when launched with "--server") exposes
+// a single task-supporting tool "AnalyzeDataset" that simulates ~15 seconds of work. The
+// client (default mode) connects to it over stdio via Microsoft.Agents.AI.Mcp's
+// McpClientTaskExtensions.ListAgentToolsWithTaskSupportAsync, hands the wrapped tools to a
+// ChatClientAgent, and exercises both invocation styles:
+//   * RunAsync          — blocks until the agent's final response is ready.
+//   * RunStreamingAsync — yields response updates as the model produces them; the model
+//                         still waits for the tool's terminal result before it can begin
+//                         producing the final answer, so the perceived "pause" reflects
+//                         tool execution time, not stream-channel latency.
+//
+// In both cases the wrapper transparently:
+//   1. Calls tools/call with task augmentation (CallToolAsTaskAsync)
+//   2. Polls tasks/get until terminal (PollTaskUntilCompleteAsync)
+//   3. Fetches tasks/result and returns the final result to the function-calling loop
+//
+// No application-level loop or continuation tokens are required in either mode.
+
+using System.ComponentModel;
+using Azure.AI.OpenAI;
+using Azure.Identity;
+using Microsoft.Agents.AI;
+using Microsoft.Agents.AI.Mcp;
+using Microsoft.Extensions.AI;
+using Microsoft.Extensions.DependencyInjection;
+using Microsoft.Extensions.Hosting;
+using Microsoft.Extensions.Logging;
+using ModelContextProtocol;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+using ModelContextProtocol.Server;
+using OpenAI.Chat;
+
+if (args.Length > 0 && args[0] == "--server")
+{
+    await RunMcpServerAsync();
+    return;
+}
+
+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";
+
+// Launch this same assembly as a stdio MCP server in a child process.
+var thisAssemblyPath = typeof(Program).Assembly.Location;
+await using var mcpClient = await McpClient.CreateAsync(new StdioClientTransport(new()
+{
+    Name = "DatasetAnalyzer",
+    Command = "dotnet",
+    Arguments = [thisAssemblyPath, "--server"],
+}));
+
+// Wrap each MCP tool with task-aware behavior. The wrapper inspects the server's
+// execution.taskSupport on each tool and, when it is Required, drives the task lifecycle
+// transparently within the agent's tool loop. Tools that don't require task semantics are
+// returned as-is and invoked inline.
+var taskOptions = new McpTaskOptions
+{
+    DefaultTimeToLive = TimeSpan.FromMinutes(5),
+};
+var mcpTools = await mcpClient.ListAgentToolsWithTaskSupportAsync(taskOptions);
+
+// 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())
+     .GetChatClient(deploymentName)
+     .AsAIAgent(
+        instructions: "You answer data-analysis questions by invoking the available tools. Always invoke a tool when one matches the request.",
+        tools: [.. mcpTools.Cast<AITool>()]);
+
+const string Prompt = "Analyze the dataset named 'sales-2025-q1' and summarize the findings.";
+
+Console.WriteLine("=== Transparent long-running MCP task (RunAsync) ===");
+Console.WriteLine("Asking the agent to analyze a dataset; the tool takes ~15s to complete.");
+Console.WriteLine("RunAsync blocks while the wrapper polls the task to completion.");
+Console.WriteLine();
+
+var stopwatch = System.Diagnostics.Stopwatch.StartNew();
+var response = await agent.RunAsync(Prompt);
+stopwatch.Stop();
+
+Console.WriteLine($"Agent response (after {stopwatch.Elapsed.TotalSeconds:F1}s):");
+Console.WriteLine(response.Text);
+
+Console.WriteLine();
+Console.WriteLine("=== Transparent long-running MCP task (RunStreamingAsync) ===");
+Console.WriteLine("Same request via the streaming API. Updates only begin to arrive after the");
+Console.WriteLine("tool's task reaches the Completed state, since the model needs the tool result");
+Console.WriteLine("before it can produce its final answer.");
+Console.WriteLine();
+
+stopwatch.Restart();
+await foreach (var update in agent.RunStreamingAsync(Prompt))
+{
+    Console.Write(update.Text);
+}
+stopwatch.Stop();
+
+Console.WriteLine();
+Console.WriteLine($"(Streaming completed after {stopwatch.Elapsed.TotalSeconds:F1}s.)");
+
+// --- Server mode (launched as a child process via --server) ---------------------------------
+static async Task RunMcpServerAsync()
+{
+    var builder = Host.CreateApplicationBuilder();
+
+    // Critical for stdio transport: any provider that writes to stdout will corrupt the
+    // JSON-RPC channel. Clear all providers; the MCP SDK routes its own diagnostics
+    // appropriately.
+    builder.Logging.ClearProviders();
+    builder.Logging.AddConsole(o => o.LogToStandardErrorThreshold = LogLevel.Trace);
+
+    builder.Services.AddMcpServer(o =>
+    {
+        o.TaskStore = new InMemoryMcpTaskStore();
+        o.ServerInfo = new Implementation { Name = "DatasetAnalyzer", Version = "1.0.0" };
+    })
+    .WithStdioServerTransport()
+    .WithTools<DatasetAnalysisTools>();
+
+    await builder.Build().RunAsync();
+}
+
+#pragma warning disable CA1812 // Discovered by MCP SDK via [McpServerToolType] attribute
+[McpServerToolType]
+internal sealed class DatasetAnalysisTools
+#pragma warning restore CA1812
+{
+    [McpServerTool(Name = "AnalyzeDataset", TaskSupport = ToolTaskSupport.Required)]
+    [Description("Analyze a tabular dataset and return summary statistics. This tool simulates a long-running analytic job (~15 seconds).")]
+    public static async Task<string> AnalyzeDatasetAsync(
+        [Description("The dataset identifier, e.g. 'sales-2025-q1'.")] string datasetName,
+        CancellationToken cancellationToken)
+    {
+        await Task.Delay(TimeSpan.FromSeconds(15), cancellationToken).ConfigureAwait(false);
+
+        return $"Findings for '{datasetName}': 12,403 rows; avg revenue $48,712; 3 anomalies detected in week 7; outliers concentrated in EMEA region.";
+    }
+}

dotnet/samples/02-agents/ModelContextProtocol/Agent_MCP_LongRunningTask_Client/README.md

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+# Agent with MCP long-running task (transparent polling)
+
+This sample demonstrates Microsoft Agent Framework's MCP long-running task support: an agent invokes an MCP tool whose execution takes too long for a single request/response cycle, and the framework polls it to completion behind the function-calling loop. From the agent's perspective the tool simply returns its result.
+
+## What this sample shows
+
+- Using `McpClient.ListAgentToolsWithTaskSupportAsync(...)` (in `Microsoft.Agents.AI.Mcp`) to wrap MCP tools with task-aware behavior.
+- Configuring `McpTaskOptions.DefaultTimeToLive` to bound the server-side task.
+- Hosting a small MCP server (in this same executable, launched with `--server`) that advertises `execution.taskSupport=required` on a tool that sleeps for ~15 seconds.
+- No application-level polling, continuation tokens, or `AllowBackgroundResponses` flag are required.
+
+The decorator drives the lifecycle internally:
+
+1. `tools/call` augmented with task metadata (`CallToolAsTaskAsync`)
+2. `tasks/get` polled until terminal (`PollTaskUntilCompleteAsync`)
+3. `tasks/result` retrieved (`GetTaskResultAsync`) and returned to the function-calling loop
+
+The sample exercises both invocation styles against the same wrapper:
+
+- `agent.RunAsync(...)` blocks until the tool completes (~15 seconds in this sample) and returns the final response.
+- `agent.RunStreamingAsync(...)` returns immediately and yields `AgentResponseUpdate` chunks as the model emits them; in this scenario the model only begins streaming its answer once the wrapped tool's task reaches the `Completed` state, so the perceived "pause" before tokens arrive reflects tool execution time, not stream-channel latency.
+
+# Prerequisites
+
+- .NET 10 SDK or later
+- Azure OpenAI service endpoint and a chat-completions deployment
+- Azure CLI installed and authenticated (`az login`)
+
+Set the following environment variables:
+
+```powershell
+$env:AZURE_OPENAI_ENDPOINT="https://your-resource.openai.azure.com/"
+$env:AZURE_OPENAI_DEPLOYMENT_NAME="gpt-5.4-mini"  # optional; defaults to gpt-5.4-mini
+```
+
+# Running
+
+```powershell
+cd Agent_MCP_LongRunningTask_Client
+dotnet run
+```
+
+You should see output similar to:
+
+```
+=== Transparent long-running MCP task (RunAsync) ===
+Asking the agent to analyze a dataset; the tool takes ~15s to complete.
+RunAsync blocks while the wrapper polls the task to completion.
+
+Agent response (after 15.4s):
+The 'sales-2025-q1' dataset contains 12,403 rows ...
+
+=== Transparent long-running MCP task (RunStreamingAsync) ===
+Same request via the streaming API. Updates only begin to arrive after the
+tool's task reaches the Completed state, since the model needs the tool result
+before it can produce its final answer.
+
+The 'sales-2025-q1' dataset contains 12,403 rows ...
+(Streaming completed after 15.7s.)
+```