chore: import upstream snapshot with attribution

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/AgentAvailableAsMCPToolSample.cs

@@ -0,0 +1,66 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol.Client;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use SK agent available as MCP tool.
+/// </summary>
+internal sealed class AgentAvailableAsMCPToolSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use SK agent available as MCP tool.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of tools provided by the MCP server.
+    /// 3. Creates a kernel and registers the MCP tools as Kernel functions.
+    /// 4. Sends the prompt to AI model together with the MCP tools represented as Kernel functions.
+    /// 5. The AI model calls the `Agents_SalesAssistant` function, which calls the MCP tool that calls the SK agent on the server.
+    /// 6. The agent calls the `OrderProcessingUtils-PlaceOrder` function to place the order for the `Grande Mug`.
+    /// 7. The agent calls the `OrderProcessingUtils-ReturnOrder` function to return the `Wide Rim Mug`.
+    /// 8. The agent summarizes the transactions and returns the result as part of the `Agents_SalesAssistant` function call.
+    /// 9. Having received the result from the `Agents_SalesAssistant`, the AI model returns the answer to the prompt.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(AgentAvailableAsMCPToolSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve and display the list provided by the MCP server
+        IList<McpClientTool> tools = await mcpClient.ListToolsAsync();
+        DisplayTools(tools);
+
+        // Create a kernel and register the MCP tools
+        Kernel kernel = CreateKernelWithChatCompletionService();
+        kernel.Plugins.AddFromFunctions("Tools", tools.Select(aiFunction => aiFunction.AsKernelFunction()));
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0,
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        string prompt = "I'd like to order the 'Grande Mug' and return the 'Wide Rim Mug' bought last week.";
+        Console.WriteLine(prompt);
+
+        // Execute a prompt using the MCP tools. The AI model will automatically call the appropriate MCP tools to answer the prompt.
+        FunctionResult result = await kernel.InvokePromptAsync(prompt, new(executionSettings));
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is: The order for the "Grande Mug" has been successfully placed.
+        // Additionally, the return process for the "Wide Rim Mug" has been successfully initiated.
+        // If you have any further questions or need assistance with anything else, feel free to ask!
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/AzureAIAgentWithMCPToolsSample.cs

@@ -0,0 +1,108 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading.Tasks;
+using Azure.AI.Agents.Persistent;
+using Azure.Identity;
+using Microsoft.Extensions.Configuration;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Agents;
+using Microsoft.SemanticKernel.Agents.AzureAI;
+using ModelContextProtocol.Client;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use <see cref="AzureAIAgent"/> with MCP tools represented as Kernel functions.
+/// </summary>
+internal sealed class AzureAIAgentWithMCPToolsSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use <see cref="AzureAIAgent"/> with MCP tools represented as Kernel functions.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of tools provided by the MCP server.
+    /// 3. Creates a kernel and registers the MCP tools as Kernel functions.
+    /// 4. Defines Azure AI agent with instructions, name, kernel, and arguments.
+    /// 5. Invokes the agent with a prompt.
+    /// 6. The agent sends the prompt to the AI model, together with the MCP tools represented as Kernel functions.
+    /// 7. The AI model calls DateTimeUtils-GetCurrentDateTimeInUtc function to get the current date time in UTC required as an argument for the next function.
+    /// 8. The AI model calls WeatherUtils-GetWeatherForCity function with the current date time and the `Boston` arguments extracted from the prompt to get the weather information.
+    /// 9. Having received the weather information from the function call, the AI model returns the answer to the agent and the agent returns the answer to the user.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(AzureAIAgentWithMCPToolsSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve and display the list provided by the MCP server
+        IList<McpClientTool> tools = await mcpClient.ListToolsAsync();
+        DisplayTools(tools);
+
+        // Create a kernel and register the MCP tools as Kernel functions
+        Kernel kernel = new();
+        kernel.Plugins.AddFromFunctions("Tools", tools.Select(aiFunction => aiFunction.AsKernelFunction()));
+
+        // Define the agent using the kernel with registered MCP tools
+        AzureAIAgent agent = await CreateAzureAIAgentAsync(
+            name: "WeatherAgent",
+            instructions: "Answer questions about the weather.",
+            kernel: kernel
+        );
+
+        // Invokes agent with a prompt
+        string prompt = "What is the likely color of the sky in Boston today?";
+        Console.WriteLine(prompt);
+
+        AgentResponseItem<ChatMessageContent> response = await agent.InvokeAsync(message: prompt).FirstAsync();
+        Console.WriteLine(response.Message);
+        Console.WriteLine();
+
+        // The expected output is: Today in Boston, the weather is 61°F and rainy. Due to the rain, the likely color of the sky will be gray.
+
+        // Delete the agent thread after use
+        await response!.Thread.DeleteAsync();
+
+        // Delete the agent after use
+        await agent.Client.Administration.DeleteAgentAsync(agent.Id);
+    }
+
+    /// <summary>
+    /// Creates an instance of <see cref="AzureAIAgent"/> with the specified name and instructions.
+    /// </summary>
+    /// <param name="kernel">The kernel instance.</param>
+    /// <param name="name">The name of the agent.</param>
+    /// <param name="instructions">The instructions for the agent.</param>
+    /// <returns>An instance of <see cref="AzureAIAgent"/>.</returns>
+    private static async Task<AzureAIAgent> CreateAzureAIAgentAsync(Kernel kernel, string name, string instructions)
+    {
+        // Load and validate configuration
+        IConfigurationRoot config = new ConfigurationBuilder()
+            .AddUserSecrets<Program>()
+            .AddEnvironmentVariables()
+            .Build();
+
+        if (config["AzureAI:Endpoint"] is not { } endpoint)
+        {
+            const string Message = "Please provide a valid `AzureAI:ConnectionString` secret to run this sample. See the associated README.md for more details.";
+            Console.Error.WriteLine(Message);
+            throw new InvalidOperationException(Message);
+        }
+
+        string modelId = config["AzureAI:ChatModelId"] ?? "gpt-4o-mini";
+
+        // Create the Azure AI Agent
+        PersistentAgentsClient agentsClient = AzureAIAgent.CreateAgentsClient(endpoint, new AzureCliCredential());
+
+        PersistentAgent agent = await agentsClient.Administration.CreateAgentAsync(modelId, name, null, instructions);
+
+        return new AzureAIAgent(agent, agentsClient)
+        {
+            Kernel = kernel
+        };
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/BaseSample.cs

@@ -0,0 +1,133 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.IO;
+using System.Threading;
+using System.Threading.Tasks;
+using Microsoft.Extensions.Configuration;
+using Microsoft.SemanticKernel;
+using ModelContextProtocol;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+
+namespace MCPClient.Samples;
+
+internal abstract class BaseSample
+{
+    /// <summary>
+    /// Creates an MCP client and connects it to the MCPServer server.
+    /// </summary>
+    /// <param name="kernel">Optional kernel instance to use for the MCP client.</param>
+    /// <param name="samplingRequestHandler">Optional handler for MCP sampling requests.</param>
+    /// <returns>An instance of <see cref="IMcpClient"/>.</returns>
+    protected static Task<McpClient> CreateMcpClientAsync(
+        Kernel? kernel = null,
+        Func<Kernel, CreateMessageRequestParams?, IProgress<ProgressNotificationValue>, CancellationToken, Task<CreateMessageResult>>? samplingRequestHandler = null)
+    {
+        KernelFunction? skSamplingHandler = null;
+
+        // Create and return the MCP client
+        return McpClient.CreateAsync(
+            clientTransport: new StdioClientTransport(new StdioClientTransportOptions
+            {
+                Name = "MCPServer",
+                Command = GetMCPServerPath(), // Path to the MCPServer executable
+            }),
+            clientOptions: samplingRequestHandler != null ? new McpClientOptions()
+            {
+                Handlers = new()
+                {
+                    SamplingHandler = InvokeHandlerAsync,
+                },
+            } : null
+         );
+
+        async ValueTask<CreateMessageResult> InvokeHandlerAsync(CreateMessageRequestParams? request, IProgress<ProgressNotificationValue> progress, CancellationToken cancellationToken)
+        {
+            if (request is null)
+            {
+                throw new ArgumentNullException(nameof(request));
+            }
+
+            skSamplingHandler ??= KernelFunctionFactory.CreateFromMethod(
+                (CreateMessageRequestParams? request, IProgress<ProgressNotificationValue> progress, CancellationToken ct) =>
+                {
+                    return samplingRequestHandler(kernel!, request, progress, ct);
+                },
+                "MCPSamplingHandler"
+            );
+
+            // The argument names must match the parameter names of the delegate the SK Function is created from
+            KernelArguments kernelArguments = new()
+            {
+                ["request"] = request,
+                ["progress"] = progress
+            };
+
+            FunctionResult functionResult = await skSamplingHandler.InvokeAsync(kernel!, kernelArguments, cancellationToken);
+
+            return functionResult.GetValue<CreateMessageResult>()!;
+        }
+    }
+
+    /// <summary>
+    /// Creates an instance of <see cref="Kernel"/> with the OpenAI chat completion service registered.
+    /// </summary>
+    /// <returns>An instance of <see cref="Kernel"/>.</returns>
+    protected static Kernel CreateKernelWithChatCompletionService()
+    {
+        // Load and validate configuration
+        IConfigurationRoot config = new ConfigurationBuilder()
+            .AddUserSecrets<Program>()
+            .AddEnvironmentVariables()
+            .Build();
+
+        if (config["OpenAI:ApiKey"] is not { } apiKey)
+        {
+            const string Message = "Please provide a valid OpenAI:ApiKey to run this sample. See the associated README.md for more details.";
+            Console.Error.WriteLine(Message);
+            throw new InvalidOperationException(Message);
+        }
+
+        string modelId = config["OpenAI:ChatModelId"] ?? "gpt-4o-mini";
+
+        // Create kernel
+        var kernelBuilder = Kernel.CreateBuilder();
+        kernelBuilder.Services.AddOpenAIChatCompletion(modelId: modelId, apiKey: apiKey);
+
+        return kernelBuilder.Build();
+    }
+
+    /// <summary>
+    /// Displays the list of available MCP tools.
+    /// </summary>
+    /// <param name="tools">The list of the tools to display.</param>
+    protected static void DisplayTools(IList<McpClientTool> tools)
+    {
+        Console.WriteLine("Available MCP tools:");
+        foreach (var tool in tools)
+        {
+            Console.WriteLine($"- Name: {tool.Name}, Description: {tool.Description}");
+        }
+        Console.WriteLine();
+    }
+
+    /// <summary>
+    /// Returns the path to the MCPServer server executable.
+    /// </summary>
+    /// <returns>The path to the MCPServer server executable.</returns>
+    private static string GetMCPServerPath()
+    {
+        // Determine the configuration (Debug or Release)  
+        string configuration;
+
+#if DEBUG
+        configuration = "Debug";
+#else
+        configuration = "Release";
+#endif
+
+        return Path.Combine("..", "..", "..", "..", "MCPServer", "bin", configuration, "net8.0", "MCPServer.exe");
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/ChatCompletionAgentWithMCPToolsSample.cs

@@ -0,0 +1,73 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Agents;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol.Client;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use <see cref="ChatCompletionAgent"/> with MCP tools represented as Kernel functions.
+/// </summary>
+internal sealed class ChatCompletionAgentWithMCPToolsSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use <see cref="ChatCompletionAgent"/> with MCP tools represented as Kernel functions.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of tools provided by the MCP server.
+    /// 3. Creates a kernel and registers the MCP tools as Kernel functions.
+    /// 4. Defines chat completion agent with instructions, name, kernel, and arguments.
+    /// 5. Invokes the agent with a prompt.
+    /// 6. The agent sends the prompt to the AI model, together with the MCP tools represented as Kernel functions.
+    /// 7. The AI model calls DateTimeUtils-GetCurrentDateTimeInUtc function to get the current date time in UTC required as an argument for the next function.
+    /// 8. The AI model calls WeatherUtils-GetWeatherForCity function with the current date time and the `Boston` arguments extracted from the prompt to get the weather information.
+    /// 9. Having received the weather information from the function call, the AI model returns the answer to the agent and the agent returns the answer to the user.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(ChatCompletionAgentWithMCPToolsSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve and display the list provided by the MCP server
+        IList<McpClientTool> tools = await mcpClient.ListToolsAsync();
+        DisplayTools(tools);
+
+        // Create a kernel and register the MCP tools as kernel functions
+        Kernel kernel = CreateKernelWithChatCompletionService();
+        kernel.Plugins.AddFromFunctions("Tools", tools.Select(aiFunction => aiFunction.AsKernelFunction()));
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        string prompt = "What is the likely color of the sky in Boston today?";
+        Console.WriteLine(prompt);
+
+        // Define the agent
+        ChatCompletionAgent agent = new()
+        {
+            Instructions = "Answer questions about the weather.",
+            Name = "WeatherAgent",
+            Kernel = kernel,
+            Arguments = new KernelArguments(executionSettings),
+        };
+
+        // Invokes agent with a prompt
+        ChatMessageContent response = await agent.InvokeAsync(prompt).FirstAsync();
+
+        Console.WriteLine(response);
+        Console.WriteLine();
+
+        // The expected output is: The sky in Boston today is likely gray due to rainy weather.
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/MCPPromptSample.cs

@@ -0,0 +1,78 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use the Model Context Protocol (MCP) prompt with the Semantic Kernel.
+/// </summary>
+internal sealed class MCPPromptSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use the MCP prompt with the Semantic Kernel.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of prompts provided by the MCP server.
+    /// 3. Gets the current weather for Boston and Sydney using the `GetCurrentWeatherForCity` prompt.
+    /// 4. Adds the MCP server prompts to the chat history and prompts the AI model to compare the weather in the two cities and suggest the best place to go for a walk.
+    /// 5. After receiving and processing the weather data for both cities and the prompt, the AI model returns an answer.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(MCPPromptSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve and display the list of prompts provided by the MCP server
+        IList<McpClientPrompt> prompts = await mcpClient.ListPromptsAsync();
+        DisplayPrompts(prompts);
+
+        // Create a kernel
+        Kernel kernel = CreateKernelWithChatCompletionService();
+
+        // Get weather for Boston using the `GetCurrentWeatherForCity` prompt from the MCP server
+        GetPromptResult bostonWeatherPrompt = await mcpClient.GetPromptAsync("GetCurrentWeatherForCity", new Dictionary<string, object?>() { ["city"] = "Boston", ["time"] = DateTime.UtcNow.ToString() });
+
+        // Get weather for Sydney using the `GetCurrentWeatherForCity` prompt from the MCP server
+        GetPromptResult sydneyWeatherPrompt = await mcpClient.GetPromptAsync("GetCurrentWeatherForCity", new Dictionary<string, object?>() { ["city"] = "Sydney", ["time"] = DateTime.UtcNow.ToString() });
+
+        // Add the prompts to the chat history
+        ChatHistory chatHistory = [];
+        chatHistory.AddRange(bostonWeatherPrompt.ToChatMessageContents());
+        chatHistory.AddRange(sydneyWeatherPrompt.ToChatMessageContents());
+        chatHistory.AddUserMessage("Compare the weather in the two cities and suggest the best place to go for a walk.");
+
+        // Execute a prompt using the MCP tools and prompt
+        IChatCompletionService chatCompletion = kernel.GetRequiredService<IChatCompletionService>();
+
+        ChatMessageContent result = await chatCompletion.GetChatMessageContentAsync(chatHistory, kernel: kernel);
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is: Given these conditions, Sydney would be the better choice for a pleasant walk, as the sunny and warm weather is ideal for outdoor activities.
+        // The rain in Boston could make walking less enjoyable and potentially inconvenient.
+    }
+
+    /// <summary>
+    /// Displays the list of available MCP prompts.
+    /// </summary>
+    /// <param name="prompts">The list of the prompts to display.</param>
+    private static void DisplayPrompts(IList<McpClientPrompt> prompts)
+    {
+        Console.WriteLine("Available MCP prompts:");
+        foreach (var prompt in prompts)
+        {
+            Console.WriteLine($"- Name: {prompt.Name}, Description: {prompt.Description}");
+        }
+        Console.WriteLine();
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/MCPResourceTemplatesSample.cs

@@ -0,0 +1,83 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use the Model Context Protocol (MCP) resource templates with the Semantic Kernel.
+/// </summary>
+internal sealed class MCPResourceTemplatesSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use the MCP resource templates with the Semantic Kernel.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of resource templates provided by the MCP server.
+    /// 3. Reads relevant to the prompt records from the `vectorStore://records/{prompt}` MCP resource template.
+    /// 4. Adds the records to the chat history and prompts the AI model to explain what SK is.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(MCPResourceTemplatesSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve list of resource templates provided by the MCP server and display them
+        IList<McpClientResourceTemplate> resourceTemplates = await mcpClient.ListResourceTemplatesAsync();
+        DisplayResourceTemplates(resourceTemplates);
+
+        // Create a kernel
+        Kernel kernel = CreateKernelWithChatCompletionService();
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0,
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        string prompt = "What is the Semantic Kernel?";
+
+        // Retrieve relevant to the prompt records via MCP resource template
+        ReadResourceResult resource = await mcpClient.ReadResourceAsync(new Uri($"vectorStore://records/{prompt}"));
+
+        // Add the resource content/records to the chat history and prompt the AI model to explain what SK is
+        ChatHistory chatHistory = [];
+        chatHistory.AddUserMessage(resource.ToChatMessageContentItemCollection());
+        chatHistory.AddUserMessage(prompt);
+
+        // Execute a prompt using the MCP resource and prompt added to the chat history
+        IChatCompletionService chatCompletion = kernel.GetRequiredService<IChatCompletionService>();
+
+        ChatMessageContent result = await chatCompletion.GetChatMessageContentAsync(chatHistory, executionSettings, kernel);
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is: The Semantic Kernel (SK) is a lightweight software development kit (SDK) designed for use in .NET applications.
+        // It acts as an orchestrator that facilitates interaction between AI models and available plugins, enabling them to work together to produce desired outputs.
+    }
+
+    /// <summary>
+    /// Displays the list of resource templates provided by the MCP server.
+    /// </summary>
+    /// <param name="resourceTemplates">The list of resource templates to display.</param>
+    private static void DisplayResourceTemplates(IList<McpClientResourceTemplate> resourceTemplates)
+    {
+        Console.WriteLine("Available MCP resource templates:");
+        foreach (var template in resourceTemplates)
+        {
+            Console.WriteLine($"- Name: {template.Name}, Description: {template.Description}");
+        }
+        Console.WriteLine();
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/MCPResourcesSample.cs

@@ -0,0 +1,81 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use the Model Context Protocol (MCP) resources with the Semantic Kernel.
+/// </summary>
+internal sealed class MCPResourcesSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use the MCP resources with the Semantic Kernel.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of resources provided by the MCP server.
+    /// 3. Retrieves the `image://cat.jpg` resource content from the MCP server.
+    /// 4. Adds the image to the chat history and prompts the AI model to describe the content of the image.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(MCPResourcesSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve list of resources provided by the MCP server and display them
+        IList<McpClientResource> resources = await mcpClient.ListResourcesAsync();
+        DisplayResources(resources);
+
+        // Create a kernel
+        Kernel kernel = CreateKernelWithChatCompletionService();
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0,
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        // Retrieve the `image://cat.jpg` resource from the MCP server
+        ReadResourceResult resource = await mcpClient.ReadResourceAsync(new Uri("image://cat.jpg"));
+
+        // Add the resource to the chat history and prompt the AI model to describe the content of the image
+        ChatHistory chatHistory = [];
+        chatHistory.AddUserMessage(resource.ToChatMessageContentItemCollection());
+        chatHistory.AddUserMessage("Describe the content of the image?");
+
+        // Execute a prompt using the MCP resource and prompt added to the chat history
+        IChatCompletionService chatCompletion = kernel.GetRequiredService<IChatCompletionService>();
+
+        ChatMessageContent result = await chatCompletion.GetChatMessageContentAsync(chatHistory, executionSettings, kernel);
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is: The image features a fluffy cat sitting in a lush, colorful garden.
+        // The garden is filled with various flowers and plants, creating a vibrant and serene atmosphere...
+    }
+
+    /// <summary>
+    /// Displays the list of resources provided by the MCP server.
+    /// </summary>
+    /// <param name="resources">The list of resources to display.</param>
+    private static void DisplayResources(IList<McpClientResource> resources)
+    {
+        Console.WriteLine("Available MCP resources:");
+        foreach (var resource in resources)
+        {
+            Console.WriteLine($"- Name: {resource.Name}, Uri: {resource.Uri}, Description: {resource.Description}");
+        }
+        Console.WriteLine();
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/MCPSamplingSample.cs

@@ -0,0 +1,132 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.ChatCompletion;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol;
+using ModelContextProtocol.Client;
+using ModelContextProtocol.Protocol;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// Demonstrates how to use the Model Context Protocol (MCP) sampling with the Semantic Kernel.
+/// </summary>
+internal sealed class MCPSamplingSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use the MCP sampling with the Semantic Kernel.
+    /// The code in this method:
+    /// 1. Creates an MCP client and register the sampling request handler.
+    /// 2. Retrieves the list of tools provided by the MCP server and registers them as Kernel functions.
+    /// 3. Prompts the AI model to create a schedule based on the latest unread emails in the mailbox.
+    /// 4. The AI model calls the `MailboxUtils-SummarizeUnreadEmails` function to summarize the unread emails.
+    /// 5. The `MailboxUtils-SummarizeUnreadEmails` function creates a few sample emails with attachments and
+    ///    sends a sampling request to the client to summarize them:
+    ///    5.1. The client receive sampling request from server and invokes the sampling request handler.
+    ///    5.2. SK intercepts the sampling request invocation via `HumanInTheLoopFilter` filter to enable human-in-the-loop processing.
+    ///    5.3. The `HumanInTheLoopFilter` allows invocation of the sampling request handler.
+    ///    5.5. The sampling request handler sends the sampling request to the AI model to summarize the emails.
+    ///    5.6. The AI model processes the request and returns the summary to the handler which sends it back to the server.
+    ///    5.7. The `MailboxUtils-SummarizeUnreadEmails` function receives the result and returns it to the AI model.
+    /// 7. Having received the summary, the AI model creates a schedule based on the unread emails.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(MCPSamplingSample)} sample.");
+
+        // Create a kernel
+        Kernel kernel = CreateKernelWithChatCompletionService();
+
+        // Register the human-in-the-loop filter that intercepts function calls allowing users to review and approve or reject them
+        kernel.FunctionInvocationFilters.Add(new HumanInTheLoopFilter());
+
+        // Create an MCP client with a custom sampling request handler
+        McpClient mcpClient = await CreateMcpClientAsync(kernel, SamplingRequestHandlerAsync);
+
+        // Import MCP tools as Kernel functions so AI model can call them
+        IList<McpClientTool> tools = await mcpClient.ListToolsAsync();
+        kernel.Plugins.AddFromFunctions("Tools", tools.Select(aiFunction => aiFunction.AsKernelFunction()));
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0,
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        // Execute a prompt
+        string prompt = "Create a schedule for me based on the latest unread emails in my inbox.";
+        IChatCompletionService chatCompletion = kernel.GetRequiredService<IChatCompletionService>();
+        ChatMessageContent result = await chatCompletion.GetChatMessageContentAsync(prompt, executionSettings, kernel);
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is:
+        // ### Today
+        // - **Review Sales Report:**
+        //   - **Task:** Provide feedback on the Carretera Sales Report for January to June 2014.
+        //   - **Deadline:** End of the day.
+        //   - **Details:** Check the attached spreadsheet for sales data.
+        //
+        // ### Tomorrow
+        // - **Update Employee Information:**
+        //   - **Task:** Update the list of employee birthdays and positions.
+        //   - **Deadline:** By the end of the day.
+        //   - **Details:** Refer to the attached table for employee details.
+        //
+        // ### Saturday
+        // - **Attend BBQ:**
+        //   - **Event:** BBQ Invitation
+        //   - **Details:** Join the BBQ as mentioned in the sales report email.
+        //
+        // ### Sunday
+        // - **Join Hike:**
+        //   - **Event:** Hiking Invitation
+        //   - **Details:** Participate in the hike as mentioned in the HR email.
+    }
+
+    /// <summary>
+    /// Handles sampling requests from the MCP client.
+    /// </summary>
+    /// <param name="kernel">The kernel instance.</param>
+    /// <param name="request">The sampling request.</param>
+    /// <param name="progress">The progress notification.</param>
+    /// <param name="cancellationToken">The cancellation token.</param>
+    /// <returns>The result of the sampling request.</returns>
+    private static async Task<CreateMessageResult> SamplingRequestHandlerAsync(Kernel kernel, CreateMessageRequestParams? request, IProgress<ProgressNotificationValue> progress, CancellationToken cancellationToken)
+    {
+        if (request is null)
+        {
+            throw new ArgumentNullException(nameof(request));
+        }
+
+        // Map the MCP sampling request to the Semantic Kernel prompt execution settings
+        OpenAIPromptExecutionSettings promptExecutionSettings = new()
+        {
+            Temperature = request.Temperature,
+            MaxTokens = request.MaxTokens,
+            StopSequences = request.StopSequences?.ToList(),
+        };
+
+        // Create a chat history from the MCP sampling request
+        ChatHistory chatHistory = [];
+        if (!string.IsNullOrEmpty(request.SystemPrompt))
+        {
+            chatHistory.AddSystemMessage(request.SystemPrompt);
+        }
+        chatHistory.AddRange(request.Messages.ToChatMessageContents());
+
+        // Prompt the AI model to generate a response
+        IChatCompletionService chatCompletion = kernel.GetRequiredService<IChatCompletionService>();
+        ChatMessageContent result = await chatCompletion.GetChatMessageContentAsync(chatHistory, promptExecutionSettings, cancellationToken: cancellationToken);
+
+        return result.ToCreateMessageResult();
+    }
+}

dotnet/samples/Demos/ModelContextProtocolClientServer/MCPClient/Samples/MCPToolsSample.cs

@@ -0,0 +1,62 @@
+// Copyright (c) Microsoft. All rights reserved.
+
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Threading.Tasks;
+using Microsoft.SemanticKernel;
+using Microsoft.SemanticKernel.Connectors.OpenAI;
+using ModelContextProtocol.Client;
+
+namespace MCPClient.Samples;
+
+/// <summary>
+/// This sample demonstrates how to use the Model Context Protocol (MCP) tools with the Semantic Kernel.
+/// </summary>
+internal sealed class MCPToolsSample : BaseSample
+{
+    /// <summary>
+    /// Demonstrates how to use the MCP tools with the Semantic Kernel.
+    /// The code in this method:
+    /// 1. Creates an MCP client.
+    /// 2. Retrieves the list of tools provided by the MCP server.
+    /// 3. Creates a kernel and registers the MCP tools as Kernel functions.
+    /// 4. Sends the prompt to AI model together with the MCP tools represented as Kernel functions.
+    /// 5. The AI model calls DateTimeUtils-GetCurrentDateTimeInUtc function to get the current date time in UTC required as an argument for the next function.
+    /// 6. The AI model calls WeatherUtils-GetWeatherForCity function with the current date time and the `Boston` arguments extracted from the prompt to get the weather information.
+    /// 7. Having received the weather information from the function call, the AI model returns the answer to the prompt.
+    /// </summary>
+    public static async Task RunAsync()
+    {
+        Console.WriteLine($"Running the {nameof(MCPToolsSample)} sample.");
+
+        // Create an MCP client
+        McpClient mcpClient = await CreateMcpClientAsync();
+
+        // Retrieve and display the list provided by the MCP server
+        IList<McpClientTool> tools = await mcpClient.ListToolsAsync();
+        DisplayTools(tools);
+
+        // Create a kernel and register the MCP tools
+        Kernel kernel = CreateKernelWithChatCompletionService();
+        kernel.Plugins.AddFromFunctions("Tools", tools.Select(aiFunction => aiFunction.AsKernelFunction()));
+
+        // Enable automatic function calling
+        OpenAIPromptExecutionSettings executionSettings = new()
+        {
+            Temperature = 0,
+            FunctionChoiceBehavior = FunctionChoiceBehavior.Auto(options: new() { RetainArgumentTypes = true })
+        };
+
+        string prompt = "What is the likely color of the sky in Boston today?";
+        Console.WriteLine(prompt);
+
+        // Execute a prompt using the MCP tools. The AI model will automatically call the appropriate MCP tools to answer the prompt.
+        FunctionResult result = await kernel.InvokePromptAsync(prompt, new(executionSettings));
+
+        Console.WriteLine(result);
+        Console.WriteLine();
+
+        // The expected output is: The likely color of the sky in Boston today is gray, as it is currently rainy.
+    }
+}