免费包白嫖最新DeepSeek-V3驱动的MCP与SemanticKernel实战教程 - 打造智能应用的终极指南

如果您需要深入交流了解请加入我们一块交流

https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=b7co0430-daed-441b-8b6f-99c423d91221

简单介绍

在进入教程之前我们可以先对MCP进行一个大概的了解，MCP是什么？为什么要用MCP？MCP和Function Calling有什么区别？

MCP是什么？

Model Context Protocol (MCP) 是一个开放协议，它使 LLM 应用与外部数据源和工具之间的无缝集成成为可能。无论你是构建 AI 驱动的 IDE、改善 chat 交互，还是构建自定义的 AI 工作流，MCP 提供了一种标准化的方式，将 LLM 与它们所需的上下文连接起来。

• MCP Hosts: 想要通过MCP访问数据的程序，如Claude Desktop、ide或AI工具
• MCP Clients: 与服务器保持1:1连接的协议客户端
• MCP Servers: 轻量级程序，每个程序都通过标准化的模型上下文协议公开特定的功能
• Local Data Sources: MCP服务器可以安全访问的计算机文件、数据库和服务
• Remote Services: MCP服务器可以连接的internet上可用的外部系统（例如通过api）

为什么要用MCP?

1. 扩展LLM的能力：让模型能够访问最新数据、专有信息和本地资源
2. 数据私密性和安全性：数据可以保留在本地或受控环境中，减少敏感信息共享的风险
3. 工具集成：使LLM能够调用和控制外部工具，扩展其功能范围
4. 减少幻觉：通过提供准确的上下文信息，降低模型生成虚假内容的可能性
5. 标准化：为开发者提供一致的接口，简化集成过程
6. 可扩展性：支持从简单用例到复杂企业级应用的多种场景
7. 跨语言的能力：通过标准的MCP协议我们可以使用不同语言提供的MCPServer的能力

MCP和Function Calling有什么区别？

我听过最多人问的就是MCP和Function Calling有什么区别？

特性	MCP	Function Calling
基础关系	基于Function Calling构建并扩展	作为MCP的基础技术
设计范围	更广泛的协议，处理上下文获取和工具使用	主要聚焦于调用特定函数
架构	客户端-服务器架构，支持分布式系统	通常是API参数的直接定义和调用
数据处理	可以处理大型数据集和复杂上下文	主要处理结构化的函数参数和返回值
上下文管理	专门设计用于管理和提供上下文	上下文管理不是核心功能
标准化程度	开放协议，旨在跨不同系统和模型标准化	实现方式因平台而异，标准化程度较低
应用场景	适用于需要复杂上下文和工具集成的场景	适用于调用预定义函数执行特定任务

简而言之，MCP（Model Context Protocol）是在Function Calling基础上发展而来的更全面的协议，它不仅保留了Function Calling的核心功能，还扩展了上下文获取和管理能力，为LLM提供更丰富、更标准化的环境交互能力。

开始MCPClient基础教程

前提条件，您需要先将上一个教程的MCPServer跑起来一会才能进行当前步骤，因为MCPClient是需要MCPServer提供Tools，下面我默认您已经运行了MCPServer，

然后可以访问 https://account.coreshub.cn/signup?invite=ZmpMQlZxYVU= 获取免费的DeepSeek-V3模型和白嫖GPU服务器，通过上面连接注册以后访问

https://console.coreshub.cn/xb3/maas/global-keys/ 地址获取到APIKey

然后记住我们的APIKey，开始下面的旅途

1. 创建属于您的MCPClient程序

创建一个McpClient的控制台项目

给项目添加依赖包

    <ItemGroup>
        <PackageReference Include="Microsoft.Extensions.Hosting" Version="9.0.3" />
        <PackageReference Include="Microsoft.SemanticKernel" Version="1.44.0" />
        <PackageReference Include="ModelContextProtocol" Version="0.1.0-preview.4" />
    </ItemGroup>

下面的教程我们将使用Microsoft.SemanticKernel开发，然后依赖ModelContextProtocol官方的包，然后我们需要用到Microsoft.Extensions.Hosting

开始我们的代码开发

1. 扩展SemanticKernel的MCPClient

由于SemnaticKernel默认是不能直接使用MCP的功能，那么我们需要先对它进行扩展

创建几个基础类

JsonSchema.cs

/// <summary>
/// Represents a JSON schema for a tool's input arguments.
/// <see href="https://github.com/modelcontextprotocol/specification/blob/main/schema/2024-11-05/schema.json">See the schema for details</see>
/// </summary>
internal class JsonSchema
{
    /// <summary>
    /// The type of the schema, should be "object".
    /// </summary>
    [JsonPropertyName("type")]
    public string Type { get; set; } = "object";

    /// <summary>
    /// Map of property names to property definitions.
    /// </summary>
    [JsonPropertyName("properties")]
    public Dictionary<string, JsonSchemaProperty>? Properties { get; set; }

    /// <summary>
    /// List of required property names.
    /// </summary>
    [JsonPropertyName("required")]
    public List<string>? Required { get; set; }
}
JsonSchemaProperty.cs
/// <summary>
/// Represents a property in a JSON schema.
/// <see href="https://github.com/modelcontextprotocol/specification/blob/main/schema/2024-11-05/schema.json">See the schema for details</see>
/// </summary>
internal class JsonSchemaProperty
{
    /// <summary>
    /// The type of the property. Should be a JSON Schema type and is required.
    /// </summary>
    [JsonPropertyName("type")]
    public string Type { get; set; } = string.Empty;

    /// <summary>
    /// A human-readable description of the property.
    /// </summary>
    [JsonPropertyName("description")]
    public string? Description { get; set; } = string.Empty;
}

接下来创建MCP的扩展类，用于将MCPFunction进行转换成SemanticKernel Function

ModelContextProtocolExtensions.cs

/// <summary>
/// Extension methods for ModelContextProtocol
/// </summary>
internal static class ModelContextProtocolExtensions
{
    /// <summary>
    /// Map the tools exposed on this <see cref="IMcpClient"/> to a collection of <see cref="KernelFunction"/> instances for use with the Semantic Kernel.
    /// <param name="mcpClient">The <see cref="IMcpClient"/>.</param>
    /// <param name="cancellationToken">The optional <see cref="CancellationToken"/>.</param>
    /// </summary>
    internal static async Task<IReadOnlyList<KernelFunction>> MapToFunctionsAsync(this IMcpClient mcpClient, CancellationToken cancellationToken = default)
    {
        var functions = new List<KernelFunction>();
        foreach (var tool in await mcpClient.ListToolsAsync(cancellationToken).ConfigureAwait(false))
        {
            functions.Add(tool.ToKernelFunction(mcpClient, cancellationToken));
        }

        return functions;
    }

    private static KernelFunction ToKernelFunction(this McpClientTool tool, IMcpClient mcpClient, CancellationToken cancellationToken)
    {
        async Task<string> InvokeToolAsync(Kernel kernel, KernelFunction function, KernelArguments arguments, CancellationToken ct)
        {
            try
            {
                // Convert arguments to dictionary format expected by ModelContextProtocol
                Dictionary<string, object?> mcpArguments = [];
                foreach (var arg in arguments)
                {
                    if (arg.Value is not null)
                    {
                        mcpArguments[arg.Key] = function.ToArgumentValue(arg.Key, arg.Value);
                    }
                }

                // Call the tool through ModelContextProtocol
                var result = await mcpClient.CallToolAsync(
                    tool.Name,
                    mcpArguments.AsReadOnly(),
                    cancellationToken: ct
                ).ConfigureAwait(false);

                // Extract the text content from the result
                return string.Join("\n", result.Content
                    .Where(c => c.Type == "text")
                    .Select(c => c.Text));
            }
            catch (Exception ex)
            {
                await Console.Error.WriteLineAsync($"Error invoking tool '{tool.Name}': {ex.Message}");

                // Rethrowing to allow the kernel to handle the exception
                throw;
            }
        }

        return KernelFunctionFactory.CreateFromMethod(
            method: InvokeToolAsync,
            functionName: tool.Name,
            description: tool.Description,
            parameters: tool.ToParameters(),
            returnParameter: ToReturnParameter()
        );
    }

    private static object ToArgumentValue(this KernelFunction function, string name, object value)
    {
        var parameterType = function.Metadata.Parameters.FirstOrDefault(p => p.Name == name)?.ParameterType;

        if (parameterType == null)
        {
            return value;
        }

        if (Nullable.GetUnderlyingType(parameterType) == typeof(int))
        {
            return Convert.ToInt32(value);
        }

        if (Nullable.GetUnderlyingType(parameterType) == typeof(double))
        {
            return Convert.ToDouble(value);
        }

        if (Nullable.GetUnderlyingType(parameterType) == typeof(bool))
        {
            return Convert.ToBoolean(value);
        }

        if (parameterType == typeof(List<string>))
        {
            return (value as IEnumerable<object>)?.ToList() ?? value;
        }

        if (parameterType == typeof(Dictionary<string, object>))
        {
            return (value as Dictionary<string, object>)?.ToDictionary(kvp => kvp.Key, kvp => kvp.Value) ?? value;
        }

        return value;
    }

    private static List<KernelParameterMetadata>? ToParameters(this McpClientTool tool)
    {
        var inputSchema = JsonSerializer.Deserialize<JsonSchema>(tool.JsonSchema.GetRawText());
        var properties = inputSchema?.Properties;
        if (properties == null)
        {
            return null;
        }

        HashSet<string> requiredProperties = [.. inputSchema!.Required ?? []];
        return properties.Select(kvp =>
            new KernelParameterMetadata(kvp.Key)
            {
                Description = kvp.Value.Description,
                ParameterType = ConvertParameterDataType(kvp.Value, requiredProperties.Contains(kvp.Key)),
                IsRequired = requiredProperties.Contains(kvp.Key)
            }).ToList();
    }

    private static KernelReturnParameterMetadata ToReturnParameter()
    {
        return new KernelReturnParameterMetadata
        {
            ParameterType = typeof(string),
        };
    }

    private static Type ConvertParameterDataType(JsonSchemaProperty property, bool required)
    {
        var type = property.Type switch
        {
            "string" => typeof(string),
            "integer" => typeof(int),
            "number" => typeof(double),
            "boolean" => typeof(bool),
            "array" => typeof(List<string>),
            "object" => typeof(Dictionary<string, object>),
            _ => typeof(object)
        };

        return !required && type.IsValueType ? typeof(Nullable<>).MakeGenericType(type) : type;
    }
}

然后创建SemanticKernel的扩展，对SKFunction进行扩展

KernelExtensions.cs

/// <summary>
/// Extension methods for KernelPlugin
/// </summary>
public static class KernelExtensions
{
    private static readonly ConcurrentDictionary<string, IKernelBuilderPlugins> SseMap = new();

    /// <summary>
    /// Creates a Model Content Protocol plugin from an SSE server that contains the specified MCP functions and adds it into the plugin collection.
    /// </summary>
    /// <param name="endpoint"></param>
    /// <param name="serverName"></param>
    /// <param name="cancellationToken">The optional <see cref="CancellationToken"/>.</param>
    /// <param name="plugins"></param>
    /// <returns>A <see cref="KernelPlugin"/> containing the functions.</returns>
    public static async Task<IKernelBuilderPlugins> AddMcpFunctionsFromSseServerAsync(this IKernelBuilderPlugins plugins,
        string endpoint, string serverName, CancellationToken cancellationToken = default)
    {
        var key = ToSafePluginName(serverName);

        if (SseMap.TryGetValue(key, out var sseKernelPlugin))
        {
            return sseKernelPlugin;
        }

        var mcpClient = await GetClientAsync(serverName, endpoint, null, null, cancellationToken).ConfigureAwait(false);
        var functions = await mcpClient.MapToFunctionsAsync(cancellationToken: cancellationToken).ConfigureAwait(false);

        cancellationToken.Register(() => mcpClient.DisposeAsync().ConfigureAwait(false).GetAwaiter().GetResult());

        sseKernelPlugin = plugins.AddFromFunctions(key, functions);
        return SseMap[key] = sseKernelPlugin;
    }

    private static async Task<IMcpClient> GetClientAsync(string serverName, string? endpoint,
        Dictionary<string, string>? transportOptions, ILoggerFactory? loggerFactory,
        CancellationToken cancellationToken)
    {
        var transportType = !string.IsNullOrEmpty(endpoint) ? TransportTypes.Sse : TransportTypes.StdIo;

        McpClientOptions options = new()
        {
            ClientInfo = new()
            {
                Name = $"{serverName} {transportType}Client",
                Version = "1.0.0"
            }
        };

        var config = new McpServerConfig
        {
            Id = serverName.ToLowerInvariant(),
            Name = serverName,
            Location = endpoint,
            TransportType = transportType,
            TransportOptions = transportOptions
        };

        return await McpClientFactory.CreateAsync(config, options,
            loggerFactory: loggerFactory ?? NullLoggerFactory.Instance, cancellationToken: cancellationToken);
    }

    // A plugin name can contain only ASCII letters, digits, and underscores.
    private static string ToSafePluginName(string serverName)
    {
        return Regex.Replace(serverName, @"[^\w]", "_");
    }
}

1. 实现连接MCPServer的Tools

现在我们就可以实现核心的代码了，打开我们的Program.cs

using McpClient;
using Microsoft.SemanticKernel;
using Microsoft.SemanticKernel.ChatCompletion;
using Microsoft.SemanticKernel.Connectors.OpenAI;
using ChatMessageContent = Microsoft.SemanticKernel.ChatMessageContent;

#pragma warning disable SKEXP0010

var builder = Host.CreateEmptyApplicationBuilder(settings: null);

builder.Configuration
    .AddEnvironmentVariables()
    .AddUserSecrets<Program>();

var kernelBuilder = builder.Services.AddKernel()
    .AddOpenAIChatCompletion("DeepSeek-V3", new Uri("https://openapi.coreshub.cn/v1"), "您使用的https://openapi.coreshub.cn/v1平台的APIKey");

await kernelBuilder.Plugins.AddMcpFunctionsFromSseServerAsync("http://您的MCPServerip:端口/sse", "token");

Console.ForegroundColor = ConsoleColor.Green;
Console.WriteLine("MCP Client Started!");
Console.ResetColor();

var app = builder.Build();

var kernel = app.Services.GetService<Kernel>();
var chatCompletion = app.Services.GetService<IChatCompletionService>();

PromptForInput();
while (Console.ReadLine() is string query && !"exit".Equals(query, StringComparison.OrdinalIgnoreCase))
{
    if (string.IsNullOrWhiteSpace(query))
    {
        PromptForInput();
        continue;
    }

    var history = new ChatHistory
    {
        new ChatMessageContent(AuthorRole.System, "下面如果需要计算俩个数的和，请使用我提供的工具。"),
        new ChatMessageContent(AuthorRole.User, query)
    };

    await foreach (var message in chatCompletion?.GetStreamingChatMessageContentsAsync(history,
                       new OpenAIPromptExecutionSettings()
                       {
                           ToolCallBehavior = ToolCallBehavior.AutoInvokeKernelFunctions,
                       }, kernel))
    {
        Console.Write(message.Content);
    }

    Console.WriteLine();

    PromptForInput();
}

static void PromptForInput()
{
    Console.WriteLine("Enter a command (or 'exit' to quit):");
    Console.ForegroundColor = ConsoleColor.Cyan;
    Console.Write("> ");
    Console.ResetColor();
}

然后开始启动我们的MCPClient，在此之前需要先修改代码中的参数改成自己的，然后先启动MCPServer以后，在启动我们的MCPClient

执行以后提问1+1=?然后会得到一下的结果

在执行之前我们可以先在MCPServer中的算法函数Debug，这样就可以清晰的看到进入的流程了

通过上面的案例，恭喜您您已经熟练的掌握了MCPServer+MCPClient+SemanticKernel的入门级教程

当然如果您需要更加深入的了解AI相关的教程，您可以与我们连续加入我们的AI VIP得到更多的AI相关的教程和帮助。

更多内容点击我