MCP協議，.Net 使用示例

服務器端示例

基礎服務器

以下是一個基礎的 MCP 服務器示例，它使用標準輸入輸出（stdio）作為傳輸方式，并實現了一個簡單的回顯工具：

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);// 配置日志輸出到標準錯誤builder.Logging.AddConsole(consoleLogOptions =>{consoleLogOptions.LogToStandardErrorThreshold = LogLevel.Trace;});// 配置 MCP 服務器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");}
}

這個示例展示了如何創建一個基本的 MCP 服務器，它包含三個簡單的工具：回顯消息、加法計算和獲取當前日期時間。

文件操作工具

以下是一個實現文件操作功能的 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();}
}

這個示例實現了三個文件操作工具：讀取文本文件、寫入文本文件和列出目錄中的文件。這些工具可以幫助 AI 模型訪問和操作本地文件系統。

Web 請求工具

以下是一個實現 Web 請求功能的 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：這是一個示例實現，實際應用中應該使用真實的搜索 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);// 解析搜索結果（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}";}}
}

這個示例實現了三個 Web 請求工具：獲取 URL 內容、執行 Web 搜索和向 URL 發送 POST 請求。這些工具可以幫助 AI 模型訪問互聯網上的信息。

注冊 HttpClient：

要使用上述 Web 工具，需要在服務配置中注冊 HttpClient：

builder.Services.AddHttpClient();

數據庫工具

以下是一個實現數據庫操作功能的 MCP 工具示例（使用 Entity Framework Core）：

// 數據庫上下文
public class AppDbContext : DbContext
{public AppDbContext(DbContextOptions options) : base(options) { }public DbSet Products { get; set; }
}// 產品實體
public class Product
{public int Id { get; set; }public string Name { get; set; }public decimal Price { get; set; }public int Stock { get; set; }
}// 數據庫工具
[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}";}
}

這個示例實現了三個數據庫操作工具：獲取產品列表、搜索產品和添加新產品。這些工具可以幫助 AI 模型訪問和操作數據庫。

注冊數據庫上下文：

要使用上述數據庫工具，需要在服務配置中注冊數據庫上下文：

builder.Services.AddDbContext(options =>options.UseSqlite("Data Source=app.db"));// 注冊數據庫工具
builder.Services.AddTransient();

客戶端示例

基礎客戶端

以下是一個基礎的 MCP 客戶端示例，它連接到 MCP 服務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){// 解析命令行參數var (command, arguments) = GetCommandAndArguments(args);// 創建 MCP 客戶端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();}// 保持程序運行，等待用戶輸入Console.WriteLine("Press Enter to exit...");Console.ReadLine();}private static (string, string) GetCommandAndArguments(string[] args){// 解析命令行參數的邏輯// ...// 示例返回值return ("dotnet", "run --project ../BasicMcpServer/BasicMcpServer.csproj");}}
}

這個示例展示了如何創建一個基本的 MCP 客戶端，連接到 MCP 服務器，并列出可用的工具及其參數。

工具發現與調用

以下是一個展示如何發現和調用 MCP 工具的示例：

// 連接到 MCP 服務器
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}");
}// 調用 Echo 工具
Console.WriteLine("\nCalling Echo tool...");
var echoResult = await mcpClient.CallToolAsync("echo",new Dictionary() { ["message"] = "Hello MCP!" },CancellationToken.None);
Console.WriteLine($"Echo result: {echoResult.Content.First(c => c.Type == "text").Text}");// 調用 Add 工具
Console.WriteLine("\nCalling Add tool...");
var addResult = await mcpClient.CallToolAsync("add",new Dictionary() { ["a"] = 5, ["b"] = 7 },CancellationToken.None);
Console.WriteLine($"Add result: {addResult.Content.First(c => c.Type == "text").Text}");// 調用 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}");

這個示例展示了如何列出可用的 MCP 工具，并調用不同類型的工具，包括帶參數和不帶參數的工具。

錯誤處理

以下是一個展示如何處理 MCP 工具調用錯誤的示例：

try
{// 嘗試調用不存在的工具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($"Error Data: {ex.Data}");
}try
{// 嘗試調用工具但缺少必要參數var result = await mcpClient.CallToolAsync("echo",new Dictionary(),  // 缺少 message 參數CancellationToken.None);
}
catch (McpException ex)
{Console.WriteLine($"MCP Error: {ex.Message}");
}try
{// 嘗試調用工具但參數類型錯誤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}");
}

這個示例展示了如何處理 MCP 工具調用中可能出現的各種錯誤，包括調用不存在的工具、缺少必要參數和參數類型錯誤。

LLM 集成示例

Claude 集成

以下是一個將 MCP 工具與 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();// 創建 MCP 客戶端
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,}
});// 獲取可用工具
var tools = await mcpClient.ListToolsAsync();
Console.WriteLine($"Connected to server with {tools.Count} tools");// 創建 Claude 客戶端
var anthropicClient = new AnthropicClient(new APIAuthentication(builder.Configuration["ANTHROPIC_API_KEY"])).Messages.AsBuilder().UseFunctionInvocation().Build();// 配置聊天選項
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 處理查詢await foreach (var message in anthropicClient.GetStreamingResponseAsync(query, options)){Console.Write(message);}Console.WriteLine("\n");
}

這個示例展示了如何創建一個聊天應用，將 MCP 工具與 Claude 模型集成，使用戶能夠通過自然語言與 Claude 交互，而 Claude 能夠使用 MCP 工具來完成任務。

OpenAI 集成

以下是一個將 MCP 工具與 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();// 創建 MCP 客戶端
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,}
});// 獲取可用工具
var tools = await mcpClient.ListToolsAsync();
Console.WriteLine($"Connected to server with {tools.Count} tools");// 創建 OpenAI 客戶端
var openAiApi = new OpenAIAPI(builder.Configuration["OPENAI_API_KEY"]);// 轉換 MCP 工具為 OpenAI 工具格式
var openAiTools = tools.Select(tool => new OpenAI_API.Tool
{Type = "function",Function = new OpenAI_API.Function{Name = tool.Name,Description = tool.Description,Parameters = new{Type = "object",Properties = tool.Parameters?.ToDictionary(p => p.Name,p => new{Type = ConvertToJsonSchemaType(p.Type),Description = p.Description}) ?? new Dictionary(),Required = tool.Parameters?.Where(p => p.Required).Select(p => p.Name).ToArray() ?? Array.Empty()}}
}).ToList();// 創建聊天會話
var chat = openAiApi.Chat.CreateConversation();
chat.Model = "gpt-4o";
chat.RequestParameters.Tools = openAiTools;Console.WriteLine("Chat with GPT (type 'exit' to quit):");
while (true)
{Console.Write("> ");var query = Console.ReadLine();if (string.IsNullOrWhiteSpace(query) || query.Equals("exit", StringComparison.OrdinalIgnoreCase)){break;}// 添加用戶消息chat.AppendUserInput(query);// 獲取 GPT 響應var response = await chat.GetResponseFromChatbotAsync();Console.WriteLine(response);// 處理工具調用if (chat.ResponseParameters.ToolCalls?.Any() == true){foreach (var toolCall in chat.ResponseParameters.ToolCalls){if (toolCall.Type == "function"){Console.WriteLine($"\nCalling tool: {toolCall.Function.Name}");// 解析參數var parameters = JsonSerializer.Deserialize>(toolCall.Function.Arguments);// 調用 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($"Tool result: {resultText}");// 將工具結果添加到對話chat.AppendToolResult(resultText, toolCall.Id);}}// 獲取 GPT 對工具結果的響應var finalResponse = await chat.GetResponseFromChatbotAsync();Console.WriteLine($"\nFinal response: {finalResponse}");}Console.WriteLine("\n");
}// 輔助方法：將 MCP 類型轉換為 JSON Schema 類型
string ConvertToJsonSchemaType(string mcpType)
{return mcpType.ToLower() switch{"string" => "string","integer" => "integer","number" => "number","boolean" => "boolean","array" => "array","object" => "object",_ => "string"};
}

這個示例展示了如何創建一個聊天應用，將 MCP 工具與 OpenAI 的 GPT 模型集成，使用戶能夠通過自然語言與 GPT 交互，而 GPT 能夠使用 MCP 工具來完成任務。

Semantic Kernel 集成

以下是一個將 MCP 工具與 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();// 創建 MCP 客戶端
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,}
});// 獲取可用工具
var tools = await mcpClient.ListToolsAsync();
Console.WriteLine($"Connected to server with {tools.Count} tools");// 創建 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();// 注冊 MCP 工具為 Semantic Kernel 函數
foreach (var tool in tools)
{kernel.ImportPluginFromObject(new McpToolWrapper(mcpClient, tool), tool.Name);
}// 創建聊天歷史
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;}// 添加用戶消息到聊天歷史chatHistory.AddUserMessage(query);// 獲取 AI 響應var response = await kernel.InvokePromptAsync(chatHistory.ToString());Console.WriteLine(response);// 添加 AI 響應到聊天歷史chatHistory.AddAssistantMessage(response);Console.WriteLine("\n");
}// MCP 工具包裝類
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;}
}

這個示例展示了如何創建一個聊天應用，將 MCP 工具與 Microsoft Semantic Kernel 集成，使用戶能夠通過自然語言與 AI 交互，而 AI 能夠使用 MCP 工具來完成任務。

應用場景

文檔分析

以下是一個使用 MCP 工具進行文檔分析的示例：

[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{// 下載文檔var response = await httpClient.GetAsync(documentUrl, cancellationToken);response.EnsureSuccessStatusCode();var content = await response.Content.ReadAsStringAsync(cancellationToken);// 根據分析類型執行不同的分析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){// 實現文檔摘要生成邏輯// 這里可以使用 NLP 庫或調用外部 API// 示例實現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){// 實現實體提取邏輯// 這里可以使用 NLP 庫或調用外部 API// 示例實現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){// 實現情感分析邏輯// 這里可以使用 NLP 庫或調用外部 API// 示例實現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;}
}

這個示例展示了如何實現文檔分析工具，包括生成摘要、提取實體和分析情感。這些工具可以幫助 AI 模型分析和理解文檔內容。

數據處理

以下是一個使用 MCP 工具進行數據處理的示例：

[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{// 下載 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 數據var data = ParseCsv(csvContent);// 根據分析類型執行不同的處理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){// 解析過濾參數// 格式：column=valuevar filterParams = parameters.Split('=');if (filterParams.Length != 2){return "Invalid filter parameters. Format should be 'column=value'.";}var column = filterParams[0].Trim();var value = filterParams[1].Trim();// 應用過濾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";// 顯示前 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){// 解析轉換參數// 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();// 檢查列是否存在if (data.Count > 0 && !data[0].ContainsKey(column)){return $"Column '{column}' not found in data.";}// 應用轉換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}";}// 顯示前 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;}
}

這個示例展示了如何實現數據處理工具，包括計算統計信息、過濾數據和轉換數據。這些工具可以幫助 AI 模型處理和分析結構化數據。

代碼生成

以下是一個使用 MCP 工具進行代碼生成的示例：

[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 = ""){// 根據語言選擇代碼生成模板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){// 這里應該實現實際的代碼生成邏輯// 可以使用模板、規則或調用外部 API// 示例實現：生成一個簡單的 C# 類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){// 示例實現：生成一個簡單的 Python 類var className = GetClassName(description);var code = $@"# Generated Python code based on: {description}
import asyncioclass {className}:def __init__(self):# Constructorpassdef execute(self):print(f'Executing {className}...')# TODO: Implement based on description# {description}async def execute_async(self):print(f'Executing {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){// 示例實現：生成一個簡單的 JavaScript 類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){// 從描述中提取類名// 這里使用一個簡單的啟發式方法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(",", "");}
}

這個示例展示了如何實現代碼生成工具，可以根據描述生成不同編程語言的代碼。這些工具可以幫助 AI 模型生成可執行的代碼示例。

自動化任務

以下是一個使用 MCP 工具進行自動化任務的示例：

[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{// 解析時間if (!DateTime.TryParse(scheduledTime, out var scheduleDateTime)){return $"Invalid date/time format: {scheduledTime}. Please use yyyy-MM-dd HH:mm:ss format.";}// 檢查時間是否在未來if (scheduleDateTime <= DateTime.Now){return "Scheduled time must be in the future.";}// 記錄任務信息_logger.LogInformation($"Task scheduled: {taskDescription}");_logger.LogInformation($"Scheduled time: {scheduleDateTime}");_logger.LogInformation($"Repeat daily: {repeatDaily}");// 在實際應用中，這里應該將任務添加到調度系統// 例如使用 Quartz.NET 或其他任務調度庫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}");// 設置工作目錄var workDir = string.IsNullOrEmpty(workingDirectory)? Directory.GetCurrentDirectory(): workingDirectory;// 檢查工作目錄是否存在if (!Directory.Exists(workDir)){return $"Working directory does not exist: {workDir}";}// 創建進程啟動信息var processStartInfo = new ProcessStartInfo{FileName = "cmd.exe",Arguments = $"/c {command}",WorkingDirectory = workDir,RedirectStandardOutput = true,RedirectStandardError = true,UseShellExecute = false,CreateNoWindow = true};// 啟動進程using var process = new Process { StartInfo = processStartInfo };process.Start();// 設置超時var timeoutTask = timeoutSeconds > 0? Task.Delay(timeoutSeconds * 1000, cancellationToken): Task.Delay(-1, cancellationToken);// 讀取輸出var outputTask = process.StandardOutput.ReadToEndAsync();var errorTask = process.StandardError.ReadToEndAsync();// 等待進程完成或超時var completedTask = await Task.WhenAny(Task.Run(() => process.WaitForExit(), cancellationToken),timeoutTask);// 檢查是否超時if (completedTask == timeoutTask && !process.HasExited){process.Kill();return $"Command timed out after {timeoutSeconds} seconds.";}// 獲取輸出var output = await outputTask;var error = await errorTask;// 返回結果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{// 檢查路徑是否存在if (!File.Exists(path) && !Directory.Exists(path)){return $"Path does not exist: {path}";}// 解析變更類型var watcherChangeTypes = ParseChangeTypes(changeTypes);// 創建文件系統監視器var watcher = new FileSystemWatcher{Path = Directory.Exists(path) ? path : Path.GetDirectoryName(path),Filter = Directory.Exists(path) ? "*.*" : Path.GetFileName(path),NotifyFilter = NotifyFilters.LastWrite | NotifyFilters.FileName | NotifyFilters.DirectoryName,IncludeSubdirectories = includeSubdirectories};// 設置事件處理程序watcher.Changed += (sender, e) => _logger.LogInformation($"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}");// 啟動監視器watcher.EnableRaisingEvents = true;// 在實際應用中，應該將監視器存儲在某個地方，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;}
}

這個示例展示了如何實現自動化任務工具，包括調度任務、運行命令和監控文件變更。這些工具可以幫助 AI 模型自動化各種系統任務。