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基础服务器
以下是一个基础的 MCP 服务器示例#xff0c;它使用标准输入输出#xff08;stdio#xff09;作为传输方式#xff0c;并实现了一个简单的回显工具#xff1a;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.H…服务器端示例
基础服务器
以下是一个基础的 MCP 服务器示例它使用标准输入输出stdio作为传输方式并实现了一个简单的回显工具
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Microsoft.Extensions.Logging;
using ModelContextProtocol.Server;
using System.ComponentModel;namespace BasicMcpServer
{public class Program{public static async Task Main(string[] args){var builder Host.CreateApplicationBuilder(args);// 配置日志输出到标准错误builder.Logging.AddConsole(consoleLogOptions {consoleLogOptions.LogToStandardErrorThreshold LogLevel.Trace;});// 配置 MCP 服务器builder.Services.AddMcpServer().WithStdioServerTransport().WithToolsFromAssembly();await builder.Build().RunAsync();}}[McpServerToolType]public static class BasicTools{[McpServerTool, Description(Echoes the message back to the client.)]public static string Echo(string message) $You said: {message};[McpServerTool, Description(Adds two numbers together.)]public static double Add([Description(First number to add)] double a, [Description(Second number to add)] double b) a b;[McpServerTool, Description(Gets the current date and time.)]public static string GetDateTime() DateTime.Now.ToString(yyyy-MM-dd HH:mm:ss);}
}
这个示例展示了如何创建一个基本的 MCP 服务器它包含三个简单的工具回显消息、加法计算和获取当前日期时间。
文件操作工具
以下是一个实现文件操作功能的 MCP 工具示例
[McpServerToolType]
public static class FileTools
{[McpServerTool, Description(Reads the content of a text file.)]public static async Task ReadTextFile([Description(Path to the file to read)] string filePath,CancellationToken cancellationToken){if (!File.Exists(filePath)){throw new FileNotFoundException($File not found: {filePath});}return await File.ReadAllTextAsync(filePath, cancellationToken);}[McpServerTool, Description(Writes text content to a file.)]public static async Task WriteTextFile([Description(Path to the file to write)] string filePath,[Description(Content to write to the file)] string content,[Description(Whether to append to the file instead of overwriting)] bool append false,CancellationToken cancellationToken default){try{if (append){await File.AppendAllTextAsync(filePath, content, cancellationToken);}else{await File.WriteAllTextAsync(filePath, content, cancellationToken);}return $Successfully wrote {content.Length} characters to {filePath};}catch (Exception ex){return $Error writing to file: {ex.Message};}}[McpServerTool, Description(Lists files in a directory.)]public static string[] ListFiles([Description(Directory path to list files from)] string directoryPath,[Description(File pattern to match (e.g., *.txt))] string pattern *.*){if (!Directory.Exists(directoryPath)){throw new DirectoryNotFoundException($Directory not found: {directoryPath});}return Directory.GetFiles(directoryPath, pattern).Select(Path.GetFileName).ToArray();}
}
这个示例实现了三个文件操作工具读取文本文件、写入文本文件和列出目录中的文件。这些工具可以帮助 AI 模型访问和操作本地文件系统。
Web 请求工具
以下是一个实现 Web 请求功能的 MCP 工具示例
[McpServerToolType]
public static class WebTools
{[McpServerTool, Description(Fetches content from a URL.)]public static async Task FetchUrl(HttpClient httpClient,[Description(URL to fetch content from)] string url,CancellationToken cancellationToken){try{var response await httpClient.GetAsync(url, cancellationToken);response.EnsureSuccessStatusCode();return await response.Content.ReadAsStringAsync(cancellationToken);}catch (Exception ex){return $Error fetching URL: {ex.Message};}}[McpServerTool, Description(Performs a web search and returns results.)]public static async Task WebSearch(HttpClient httpClient,[Description(Search query)] string query,[Description(Maximum number of results to return)] int maxResults 5,CancellationToken cancellationToken default){// 注意这是一个示例实现实际应用中应该使用真实的搜索 APIvar encodedQuery Uri.EscapeDataString(query);var url $https://api.example.com/search?q{encodedQuery}limit{maxResults};try{var response await httpClient.GetAsync(url, cancellationToken);response.EnsureSuccessStatusCode();var content await response.Content.ReadAsStringAsync(cancellationToken);// 解析搜索结果示例return $Search results for {query}:\n{content};}catch (Exception ex){return $Error performing web search: {ex.Message};}}[McpServerTool, Description(Posts data to a URL and returns the response.)]public static async Task PostToUrl(HttpClient httpClient,[Description(URL to post data to)] string url,[Description(JSON data to post)] string jsonData,CancellationToken cancellationToken){try{var content new StringContent(jsonData, System.Text.Encoding.UTF8, application/json);var response await httpClient.PostAsync(url, content, cancellationToken);response.EnsureSuccessStatusCode();return await response.Content.ReadAsStringAsync(cancellationToken);}catch (Exception ex){return $Error posting to URL: {ex.Message};}}
}
这个示例实现了三个 Web 请求工具获取 URL 内容、执行 Web 搜索和向 URL 发送 POST 请求。这些工具可以帮助 AI 模型访问互联网上的信息。
注册 HttpClient
要使用上述 Web 工具需要在服务配置中注册 HttpClient
builder.Services.AddHttpClient();
数据库工具
以下是一个实现数据库操作功能的 MCP 工具示例使用 Entity Framework Core
// 数据库上下文
public class AppDbContext : DbContext
{public AppDbContext(DbContextOptions options) : base(options) { }public DbSet Products { get; set; }
}// 产品实体
public class Product
{public int Id { get; set; }public string Name { get; set; }public decimal Price { get; set; }public int Stock { get; set; }
}// 数据库工具
[McpServerToolType]
public class DatabaseTools
{private readonly AppDbContext _dbContext;public DatabaseTools(AppDbContext dbContext){_dbContext dbContext;}[McpServerTool, Description(Gets a list of products.)]public async Task GetProducts([Description(Maximum number of products to return)] int limit 10,CancellationToken cancellationToken default){var products await _dbContext.Products.Take(limit).ToListAsync(cancellationToken);return JsonSerializer.Serialize(products, new JsonSerializerOptions{WriteIndented true});}[McpServerTool, Description(Searches for products by name.)]public async Task SearchProducts([Description(Product name to search for)] string name,CancellationToken cancellationToken default){var products await _dbContext.Products.Where(p p.Name.Contains(name)).ToListAsync(cancellationToken);return JsonSerializer.Serialize(products, new JsonSerializerOptions{WriteIndented true});}[McpServerTool, Description(Adds a new product.)]public async Task AddProduct([Description(Product name)] string name,[Description(Product price)] decimal price,[Description(Product stock)] int stock,CancellationToken cancellationToken default){var product new Product{Name name,Price price,Stock stock};_dbContext.Products.Add(product);await _dbContext.SaveChangesAsync(cancellationToken);return $Product added successfully with ID: {product.Id};}
}
这个示例实现了三个数据库操作工具获取产品列表、搜索产品和添加新产品。这些工具可以帮助 AI 模型访问和操作数据库。
注册数据库上下文
要使用上述数据库工具需要在服务配置中注册数据库上下文
builder.Services.AddDbContext(options options.UseSqlite(Data Sourceapp.db));// 注册数据库工具
builder.Services.AddTransient();
客户端示例
基础客户端
以下是一个基础的 MCP 客户端示例它连接到 MCP 服务器并列出可用的工具
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;namespace BasicMcpClient
{public class Program{public static async Task Main(string[] args){// 解析命令行参数var (command, arguments) GetCommandAndArguments(args);// 创建 MCP 客户端await using var mcpClient await McpClientFactory.CreateAsync(new(){Id demo-server,Name Demo Server,TransportType TransportTypes.StdIo,TransportOptions new(){[command] command,[arguments] arguments,}});// 列出可用工具var tools await mcpClient.ListToolsAsync();Console.WriteLine(Available tools:);foreach (var tool in tools){Console.WriteLine($- {tool.Name}: {tool.Description});if (tool.Parameters?.Any() true){Console.WriteLine( Parameters:);foreach (var param in tool.Parameters){Console.WriteLine($ - {param.Name}: {param.Description} ({param.Type}));}}Console.WriteLine();}// 保持程序运行等待用户输入Console.WriteLine(Press Enter to exit...);Console.ReadLine();}private static (string, string) GetCommandAndArguments(string[] args){// 解析命令行参数的逻辑// ...// 示例返回值return (dotnet, run --project ../BasicMcpServer/BasicMcpServer.csproj);}}
}
这个示例展示了如何创建一个基本的 MCP 客户端连接到 MCP 服务器并列出可用的工具及其参数。
工具发现与调用
以下是一个展示如何发现和调用 MCP 工具的示例
// 连接到 MCP 服务器
await using var mcpClient await McpClientFactory.CreateAsync(new()
{Id demo-server,Name Demo Server,TransportType TransportTypes.StdIo,TransportOptions new(){[command] command,[arguments] arguments,}
});// 列出可用工具
var tools await mcpClient.ListToolsAsync();
Console.WriteLine($Found {tools.Count} tools:);
foreach (var tool in tools)
{Console.WriteLine($- {tool.Name});
}// 调用 Echo 工具
Console.WriteLine(\nCalling Echo tool...);
var echoResult await mcpClient.CallToolAsync(echo,new Dictionary() { [message] Hello MCP! },CancellationToken.None);
Console.WriteLine($Echo result: {echoResult.Content.First(c c.Type text).Text});// 调用 Add 工具
Console.WriteLine(\nCalling Add tool...);
var addResult await mcpClient.CallToolAsync(add,new Dictionary() { [a] 5, [b] 7 },CancellationToken.None);
Console.WriteLine($Add result: {addResult.Content.First(c c.Type text).Text});// 调用 GetDateTime 工具
Console.WriteLine(\nCalling GetDateTime tool...);
var dateTimeResult await mcpClient.CallToolAsync(getDateTime,new Dictionary(),CancellationToken.None);
Console.WriteLine($DateTime result: {dateTimeResult.Content.First(c c.Type text).Text});
这个示例展示了如何列出可用的 MCP 工具并调用不同类型的工具包括带参数和不带参数的工具。
错误处理
以下是一个展示如何处理 MCP 工具调用错误的示例
try
{// 尝试调用不存在的工具var result await mcpClient.CallToolAsync(nonExistentTool,new Dictionary(),CancellationToken.None);
}
catch (McpException ex)
{Console.WriteLine($MCP Error: {ex.Message});Console.WriteLine($Error Code: {ex.Code});Console.WriteLine($Error Data: {ex.Data});
}try
{// 尝试调用工具但缺少必要参数var result await mcpClient.CallToolAsync(echo,new Dictionary(), // 缺少 message 参数CancellationToken.None);
}
catch (McpException ex)
{Console.WriteLine($MCP Error: {ex.Message});
}try
{// 尝试调用工具但参数类型错误var result await mcpClient.CallToolAsync(add,new Dictionary() { [a] not a number, [b] 7 },CancellationToken.None);
}
catch (McpException ex)
{Console.WriteLine($MCP Error: {ex.Message});
}
这个示例展示了如何处理 MCP 工具调用中可能出现的各种错误包括调用不存在的工具、缺少必要参数和参数类型错误。
LLM 集成示例
Claude 集成
以下是一个将 MCP 工具与 Claude 模型集成的示例
using Anthropic.SDK;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;var builder Host.CreateApplicationBuilder(args);
builder.Configuration.AddEnvironmentVariables().AddUserSecrets();// 创建 MCP 客户端
var (command, arguments) GetCommandAndArguments(args);
await using var mcpClient await McpClientFactory.CreateAsync(new()
{Id demo-server,Name Demo Server,TransportType TransportTypes.StdIo,TransportOptions new(){[command] command,[arguments] arguments,}
});// 获取可用工具
var tools await mcpClient.ListToolsAsync();
Console.WriteLine($Connected to server with {tools.Count} tools);// 创建 Claude 客户端
var anthropicClient new AnthropicClient(new APIAuthentication(builder.Configuration[ANTHROPIC_API_KEY])).Messages.AsBuilder().UseFunctionInvocation().Build();// 配置聊天选项
var options new ChatOptions
{MaxOutputTokens 1000,ModelId claude-3-5-sonnet-20240229,Tools [.. tools]
};Console.WriteLine(Chat with Claude (type exit to quit):);
while (true)
{Console.Write( );var query Console.ReadLine();if (string.IsNullOrWhiteSpace(query) || query.Equals(exit, StringComparison.OrdinalIgnoreCase)){break;}// 使用 Claude 处理查询await foreach (var message in anthropicClient.GetStreamingResponseAsync(query, options)){Console.Write(message);}Console.WriteLine(\n);
}
这个示例展示了如何创建一个聊天应用将 MCP 工具与 Claude 模型集成使用户能够通过自然语言与 Claude 交互而 Claude 能够使用 MCP 工具来完成任务。
OpenAI 集成
以下是一个将 MCP 工具与 OpenAI 模型集成的示例
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;
using OpenAI_API;
using OpenAI_API.Chat;
using System.Text.Json;var builder Host.CreateApplicationBuilder(args);
builder.Configuration.AddEnvironmentVariables().AddUserSecrets();// 创建 MCP 客户端
var (command, arguments) GetCommandAndArguments(args);
await using var mcpClient await McpClientFactory.CreateAsync(new()
{Id demo-server,Name Demo Server,TransportType TransportTypes.StdIo,TransportOptions new(){[command] command,[arguments] arguments,}
});// 获取可用工具
var tools await mcpClient.ListToolsAsync();
Console.WriteLine($Connected to server with {tools.Count} tools);// 创建 OpenAI 客户端
var openAiApi new OpenAIAPI(builder.Configuration[OPENAI_API_KEY]);// 转换 MCP 工具为 OpenAI 工具格式
var openAiTools tools.Select(tool new OpenAI_API.Tool
{Type function,Function new OpenAI_API.Function{Name tool.Name,Description tool.Description,Parameters new{Type object,Properties tool.Parameters?.ToDictionary(p p.Name,p new{Type ConvertToJsonSchemaType(p.Type),Description p.Description}) ?? new Dictionary(),Required tool.Parameters?.Where(p p.Required).Select(p p.Name).ToArray() ?? Array.Empty()}}
}).ToList();// 创建聊天会话
var chat openAiApi.Chat.CreateConversation();
chat.Model gpt-4o;
chat.RequestParameters.Tools openAiTools;Console.WriteLine(Chat with GPT (type exit to quit):);
while (true)
{Console.Write( );var query Console.ReadLine();if (string.IsNullOrWhiteSpace(query) || query.Equals(exit, StringComparison.OrdinalIgnoreCase)){break;}// 添加用户消息chat.AppendUserInput(query);// 获取 GPT 响应var response await chat.GetResponseFromChatbotAsync();Console.WriteLine(response);// 处理工具调用if (chat.ResponseParameters.ToolCalls?.Any() true){foreach (var toolCall in chat.ResponseParameters.ToolCalls){if (toolCall.Type function){Console.WriteLine($\nCalling tool: {toolCall.Function.Name});// 解析参数var parameters JsonSerializer.Deserialize(toolCall.Function.Arguments);// 调用 MCP 工具var result await mcpClient.CallToolAsync(toolCall.Function.Name,parameters,CancellationToken.None);var resultText result.Content.First(c c.Type text).Text;Console.WriteLine($Tool result: {resultText});// 将工具结果添加到对话chat.AppendToolResult(resultText, toolCall.Id);}}// 获取 GPT 对工具结果的响应var finalResponse await chat.GetResponseFromChatbotAsync();Console.WriteLine($\nFinal response: {finalResponse});}Console.WriteLine(\n);
}// 辅助方法将 MCP 类型转换为 JSON Schema 类型
string ConvertToJsonSchemaType(string mcpType)
{return mcpType.ToLower() switch{string string,integer integer,number number,boolean boolean,array array,object object,_ string};
}
这个示例展示了如何创建一个聊天应用将 MCP 工具与 OpenAI 的 GPT 模型集成使用户能够通过自然语言与 GPT 交互而 GPT 能够使用 MCP 工具来完成任务。
Semantic Kernel 集成
以下是一个将 MCP 工具与 Microsoft Semantic Kernel 集成的示例
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.DependencyInjection;
using Microsoft.Extensions.Hosting;
using Microsoft.SemanticKernel;
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;var builder Host.CreateApplicationBuilder(args);
builder.Configuration.AddEnvironmentVariables().AddUserSecrets();// 创建 MCP 客户端
var (command, arguments) GetCommandAndArguments(args);
await using var mcpClient await McpClientFactory.CreateAsync(new()
{Id demo-server,Name Demo Server,TransportType TransportTypes.StdIo,TransportOptions new(){[command] command,[arguments] arguments,}
});// 获取可用工具
var tools await mcpClient.ListToolsAsync();
Console.WriteLine($Connected to server with {tools.Count} tools);// 创建 Semantic Kernel
var kernel Kernel.CreateBuilder().AddAzureOpenAIChatCompletion(deploymentName: builder.Configuration[AZURE_OPENAI_DEPLOYMENT_NAME],endpoint: builder.Configuration[AZURE_OPENAI_ENDPOINT],apiKey: builder.Configuration[AZURE_OPENAI_API_KEY]).Build();// 注册 MCP 工具为 Semantic Kernel 函数
foreach (var tool in tools)
{kernel.ImportPluginFromObject(new McpToolWrapper(mcpClient, tool), tool.Name);
}// 创建聊天历史
var chatHistory new ChatHistory();Console.WriteLine(Chat with Semantic Kernel (type exit to quit):);
while (true)
{Console.Write( );var query Console.ReadLine();if (string.IsNullOrWhiteSpace(query) || query.Equals(exit, StringComparison.OrdinalIgnoreCase)){break;}// 添加用户消息到聊天历史chatHistory.AddUserMessage(query);// 获取 AI 响应var response await kernel.InvokePromptAsync(chatHistory.ToString());Console.WriteLine(response);// 添加 AI 响应到聊天历史chatHistory.AddAssistantMessage(response);Console.WriteLine(\n);
}// MCP 工具包装类
public class McpToolWrapper
{private readonly IMcpClient _mcpClient;private readonly McpTool _tool;public McpToolWrapper(IMcpClient mcpClient, McpTool tool){_mcpClient mcpClient;_tool tool;}[KernelFunction]public async Task ExecuteAsync(Dictionary parameters){var result await _mcpClient.CallToolAsync(_tool.Name,parameters,CancellationToken.None);return result.Content.First(c c.Type text).Text;}
}
这个示例展示了如何创建一个聊天应用将 MCP 工具与 Microsoft Semantic Kernel 集成使用户能够通过自然语言与 AI 交互而 AI 能够使用 MCP 工具来完成任务。
应用场景
文档分析
以下是一个使用 MCP 工具进行文档分析的示例
[McpServerToolType]
public static class DocumentTools
{[McpServerTool, Description(Analyzes a document and extracts key information.)]public static async Task AnalyzeDocument(HttpClient httpClient,[Description(URL of the document to analyze)] string documentUrl,[Description(Type of analysis to perform (summary, entities, sentiment))] string analysisType summary,CancellationToken cancellationToken default){try{// 下载文档var response await httpClient.GetAsync(documentUrl, cancellationToken);response.EnsureSuccessStatusCode();var content await response.Content.ReadAsStringAsync(cancellationToken);// 根据分析类型执行不同的分析return analysisType.ToLower() switch{summary await GenerateSummary(content, cancellationToken),entities await ExtractEntities(content, cancellationToken),sentiment await AnalyzeSentiment(content, cancellationToken),_ throw new ArgumentException($Unsupported analysis type: {analysisType})};}catch (Exception ex){return $Error analyzing document: {ex.Message};}}private static async Task GenerateSummary(string content, CancellationToken cancellationToken){// 实现文档摘要生成逻辑// 这里可以使用 NLP 库或调用外部 API// 示例实现var summary $Document summary (length: {content.Length} characters):\n;summary This is a placeholder for the actual document summary.;return summary;}private static async Task ExtractEntities(string content, CancellationToken cancellationToken){// 实现实体提取逻辑// 这里可以使用 NLP 库或调用外部 API// 示例实现var entities Extracted entities:\n;entities - Entity 1 (Person)\n;entities - Entity 2 (Organization)\n;entities - Entity 3 (Location);return entities;}private static async Task AnalyzeSentiment(string content, CancellationToken cancellationToken){// 实现情感分析逻辑// 这里可以使用 NLP 库或调用外部 API// 示例实现var sentiment Sentiment analysis:\n;sentiment - Overall sentiment: Positive\n;sentiment - Confidence score: 0.85\n;sentiment - Key positive phrases: [...]\n;sentiment - Key negative phrases: [...];return sentiment;}
}
这个示例展示了如何实现文档分析工具包括生成摘要、提取实体和分析情感。这些工具可以帮助 AI 模型分析和理解文档内容。
数据处理
以下是一个使用 MCP 工具进行数据处理的示例
[McpServerToolType]
public static class DataProcessingTools
{[McpServerTool, Description(Processes CSV data and performs analysis.)]public static async Task ProcessCsvData([Description(URL of the CSV file to process)] string csvUrl,[Description(Type of analysis to perform (stats, filter, transform))] string analysisType stats,[Description(Additional parameters for the analysis (e.g., filter criteria))] string parameters ,CancellationToken cancellationToken default){try{// 下载 CSV 文件using var httpClient new HttpClient();var response await httpClient.GetAsync(csvUrl, cancellationToken);response.EnsureSuccessStatusCode();var csvContent await response.Content.ReadAsStringAsync(cancellationToken);// 解析 CSV 数据var data ParseCsv(csvContent);// 根据分析类型执行不同的处理return analysisType.ToLower() switch{stats CalculateStatistics(data),filter FilterData(data, parameters),transform TransformData(data, parameters),_ throw new ArgumentException($Unsupported analysis type: {analysisType})};}catch (Exception ex){return $Error processing CSV data: {ex.Message};}}private static List ParseCsv(string csvContent){var result new List();var lines csvContent.Split(\n);if (lines.Length 2){return result;}var headers lines[0].Split(,).Select(h h.Trim()).ToArray();for (int i 1; i lines.Length; i){var line lines[i].Trim();if (string.IsNullOrEmpty(line)){continue;}var values line.Split(,).Select(v v.Trim()).ToArray();var row new Dictionary();for (int j 0; j Math.Min(headers.Length, values.Length); j){row[headers[j]] values[j];}result.Add(row);}return result;}private static string CalculateStatistics(List data){if (data.Count 0){return No data to analyze.;}var result Data Statistics:\n;result $- Row count: {data.Count}\n;result $- Column count: {data[0].Count}\n;result - Columns: string.Join(, , data[0].Keys);return result;}private static string FilterData(List data, string parameters){// 解析过滤参数// 格式columnvaluevar filterParams parameters.Split();if (filterParams.Length ! 2){return Invalid filter parameters. Format should be columnvalue.;}var column filterParams[0].Trim();var value filterParams[1].Trim();// 应用过滤var filteredData data.Where(row row.ContainsKey(column) row[column] value).ToList();var result $Filtered data (where {column} {value}):\n;result $- Matching rows: {filteredData.Count}\n\n;// 显示前 5 行for (int i 0; i Math.Min(5, filteredData.Count); i){result $Row {i 1}:\n;foreach (var kvp in filteredData[i]){result $ {kvp.Key}: {kvp.Value}\n;}result \n;}return result;}private static string TransformData(List data, string parameters){// 解析转换参数// 格式operation:columnvar transformParams parameters.Split(:);if (transformParams.Length ! 2){return Invalid transform parameters. Format should be operation:column.;}var operation transformParams[0].Trim().ToLower();var column transformParams[1].Trim();// 检查列是否存在if (data.Count 0 !data[0].ContainsKey(column)){return $Column {column} not found in data.;}// 应用转换var result $Transformed data ({operation} on {column}):\n\n;switch (operation){case uppercase:foreach (var row in data){if (row.ContainsKey(column)){row[column] row[column].ToUpper();}}break;case lowercase:foreach (var row in data){if (row.ContainsKey(column)){row[column] row[column].ToLower();}}break;default:return $Unsupported operation: {operation};}// 显示前 5 行for (int i 0; i Math.Min(5, data.Count); i){result $Row {i 1}:\n;foreach (var kvp in data[i]){result $ {kvp.Key}: {kvp.Value}\n;}result \n;}return result;}
}
这个示例展示了如何实现数据处理工具包括计算统计信息、过滤数据和转换数据。这些工具可以帮助 AI 模型处理和分析结构化数据。
代码生成
以下是一个使用 MCP 工具进行代码生成的示例
[McpServerToolType]
public static class CodeGenerationTools
{[McpServerTool, Description(Generates code based on a description.)]public static string GenerateCode([Description(Description of the code to generate)] string description,[Description(Programming language (csharp, python, javascript))] string language csharp,[Description(Additional options (e.g., framework, style))] string options ){// 根据语言选择代码生成模板var codeTemplate language.ToLower() switch{csharp GenerateCSharpCode(description, options),python GeneratePythonCode(description, options),javascript GenerateJavaScriptCode(description, options),_ throw new ArgumentException($Unsupported language: {language})};return codeTemplate;}private static string GenerateCSharpCode(string description, string options){// 这里应该实现实际的代码生成逻辑// 可以使用模板、规则或调用外部 API// 示例实现生成一个简单的 C# 类var className GetClassName(description);var code $// Generated C# code based on: {description}
using System;
using System.Threading.Tasks;namespace GeneratedCode
{{public class {className}{{public {className}(){{// Constructor}}public void Execute(){{Console.WriteLine(Executing {className}...);// TODO: Implement based on description// {description}}}public async Task ExecuteAsync(){{Console.WriteLine(Executing {className} asynchronously...);// TODO: Implement based on description// {description}await Task.CompletedTask;}}}}
}};return code;}private static string GeneratePythonCode(string description, string options){// 示例实现生成一个简单的 Python 类var className GetClassName(description);var code $# Generated Python code based on: {description}
import asyncioclass {className}:def __init__(self):# Constructorpassdef execute(self):print(fExecuting {className}...)# TODO: Implement based on description# {description}async def execute_async(self):print(fExecuting {className} asynchronously...)# TODO: Implement based on description# {description}await asyncio.sleep(0)# Usage example
if __name__ __main__:obj {className}()obj.execute()# Async usage# asyncio.run(obj.execute_async());return code;}private static string GenerateJavaScriptCode(string description, string options){// 示例实现生成一个简单的 JavaScript 类var className GetClassName(description);var code $// Generated JavaScript code based on: {description}
class {className} {{constructor() {{// Constructor}}execute() {{console.log(Executing {className}...);// TODO: Implement based on description// {description}}}async executeAsync() {{console.log(Executing {className} asynchronously...);// TODO: Implement based on description// {description}await Promise.resolve();}}
}}// Usage example
const obj new {className}();
obj.execute();// Async usage
// obj.executeAsync().then(() console.log(Done));;return code;}private static string GetClassName(string description){// 从描述中提取类名// 这里使用一个简单的启发式方法var words description.Split( ).Where(w !string.IsNullOrEmpty(w)).Select(w char.ToUpper(w[0]) w.Substring(1).ToLower()).ToArray();return string.Join(, words).Replace(., ).Replace(,, );}
}
这个示例展示了如何实现代码生成工具可以根据描述生成不同编程语言的代码。这些工具可以帮助 AI 模型生成可执行的代码示例。
自动化任务
以下是一个使用 MCP 工具进行自动化任务的示例
[McpServerToolType]
public class AutomationTools
{private readonly ILogger _logger;public AutomationTools(ILogger logger){_logger logger;}[McpServerTool, Description(Schedules a task to run at a specified time.)]public string ScheduleTask([Description(Description of the task to schedule)] string taskDescription,[Description(When to run the task (format: yyyy-MM-dd HH:mm:ss))] string scheduledTime,[Description(Whether to repeat the task daily)] bool repeatDaily false){try{// 解析时间if (!DateTime.TryParse(scheduledTime, out var scheduleDateTime)){return $Invalid date/time format: {scheduledTime}. Please use yyyy-MM-dd HH:mm:ss format.;}// 检查时间是否在未来if (scheduleDateTime DateTime.Now){return Scheduled time must be in the future.;}// 记录任务信息_logger.LogInformation($Task scheduled: {taskDescription});_logger.LogInformation($Scheduled time: {scheduleDateTime});_logger.LogInformation($Repeat daily: {repeatDaily});// 在实际应用中这里应该将任务添加到调度系统// 例如使用 Quartz.NET 或其他任务调度库return $Task scheduled successfully:\n $- Description: {taskDescription}\n $- Scheduled time: {scheduleDateTime}\n $- Repeat daily: {repeatDaily};}catch (Exception ex){_logger.LogError(ex, Error scheduling task);return $Error scheduling task: {ex.Message};}}[McpServerTool, Description(Runs a command on the server.)]public async Task RunCommand([Description(Command to run)] string command,[Description(Working directory for the command)] string workingDirectory ,[Description(Timeout in seconds (0 for no timeout))] int timeoutSeconds 60,CancellationToken cancellationToken default){try{_logger.LogInformation($Running command: {command});// 设置工作目录var workDir string.IsNullOrEmpty(workingDirectory)? Directory.GetCurrentDirectory(): workingDirectory;// 检查工作目录是否存在if (!Directory.Exists(workDir)){return $Working directory does not exist: {workDir};}// 创建进程启动信息var processStartInfo new ProcessStartInfo{FileName cmd.exe,Arguments $/c {command},WorkingDirectory workDir,RedirectStandardOutput true,RedirectStandardError true,UseShellExecute false,CreateNoWindow true};// 启动进程using var process new Process { StartInfo processStartInfo };process.Start();// 设置超时var timeoutTask timeoutSeconds 0? Task.Delay(timeoutSeconds * 1000, cancellationToken): Task.Delay(-1, cancellationToken);// 读取输出var outputTask process.StandardOutput.ReadToEndAsync();var errorTask process.StandardError.ReadToEndAsync();// 等待进程完成或超时var completedTask await Task.WhenAny(Task.Run(() process.WaitForExit(), cancellationToken),timeoutTask);// 检查是否超时if (completedTask timeoutTask !process.HasExited){process.Kill();return $Command timed out after {timeoutSeconds} seconds.;}// 获取输出var output await outputTask;var error await errorTask;// 返回结果return string.IsNullOrEmpty(error)? $Command executed successfully:\n{output}: $Command executed with errors:\n{error}\n\nOutput:\n{output};}catch (Exception ex){_logger.LogError(ex, Error running command);return $Error running command: {ex.Message};}}[McpServerTool, Description(Monitors a file or directory for changes.)]public string MonitorFileChanges([Description(Path to file or directory to monitor)] string path,[Description(Types of changes to monitor (Created, Deleted, Changed, Renamed))] string changeTypes Created,Deleted,Changed,Renamed,[Description(Whether to include subdirectories)] bool includeSubdirectories true){try{// 检查路径是否存在if (!File.Exists(path) !Directory.Exists(path)){return $Path does not exist: {path};}// 解析变更类型var watcherChangeTypes ParseChangeTypes(changeTypes);// 创建文件系统监视器var watcher new FileSystemWatcher{Path Directory.Exists(path) ? path : Path.GetDirectoryName(path),Filter Directory.Exists(path) ? *.* : Path.GetFileName(path),NotifyFilter NotifyFilters.LastWrite | NotifyFilters.FileName | NotifyFilters.DirectoryName,IncludeSubdirectories includeSubdirectories};// 设置事件处理程序watcher.Changed (sender, e) _logger.LogInformation($File changed: {e.FullPath});watcher.Created (sender, e) _logger.LogInformation($File created: {e.FullPath});watcher.Deleted (sender, e) _logger.LogInformation($File deleted: {e.FullPath});watcher.Renamed (sender, e) _logger.LogInformation($File renamed: {e.OldFullPath} to {e.FullPath});// 启动监视器watcher.EnableRaisingEvents true;// 在实际应用中应该将监视器存储在某个地方以便以后可以停止它return $File monitoring started:\n $- Path: {path}\n $- Change types: {changeTypes}\n $- Include subdirectories: {includeSubdirectories};}catch (Exception ex){_logger.LogError(ex, Error monitoring file changes);return $Error monitoring file changes: {ex.Message};}}private static NotifyFilters ParseChangeTypes(string changeTypes){var result NotifyFilters.LastWrite;foreach (var type in changeTypes.Split(,).Select(t t.Trim())){result | type.ToLower() switch{created NotifyFilters.FileName | NotifyFilters.DirectoryName,deleted NotifyFilters.FileName | NotifyFilters.DirectoryName,changed NotifyFilters.LastWrite,renamed NotifyFilters.FileName | NotifyFilters.DirectoryName,_ NotifyFilters.LastWrite};}return result;}
}
这个示例展示了如何实现自动化任务工具包括调度任务、运行命令和监控文件变更。这些工具可以帮助 AI 模型自动化各种系统任务。
结论
本文档提供了丰富的 .NET MCP 示例涵盖了服务器端实现、客户端实现、LLM 集成和各种应用场景。这些示例可以帮助开发者快速上手 MCP并将其应用到实际项目中。
MCP 的强大之处在于它提供了一种标准化的方式使 AI 模型能够安全地访问和操作各种数据源和工具。通过实现 MCP 服务器和客户端开发者可以创建功能丰富的 AI 应用使 AI 能够执行各种任务从简单的文本处理到复杂的自动化操作。
随着 MCP 生态系统的不断发展我们可以期待更多的功能和改进。官方的 C# SDK 提供了一个稳定的基础使 .NET 开发者能够轻松地实现 MCP 服务器和客户端。
