构建 MCP 客户端

echo "ANTHROPIC_API_KEY=<your key here>" > .env

echo ".env" >> .gitignore

import { Anthropic } from "@anthropic-ai/sdk";
import {
  MessageParam,
  Tool,
} from "@anthropic-ai/sdk/resources/messages/messages.mjs";
import { Client } from "@modelcontextprotocol/client";
import { StdioClientTransport } from "@modelcontextprotocol/client/stdio";
import readline from "readline/promises";
import dotenv from "dotenv";

dotenv.config();

const ANTHROPIC_API_KEY = process.env.ANTHROPIC_API_KEY;
if (!ANTHROPIC_API_KEY) {
  throw new Error("ANTHROPIC_API_KEY is not set");
}

class MCPClient {
  private mcp: Client;
  private anthropic: Anthropic;
  private transport: StdioClientTransport | null = null;
  private tools: Tool[] = [];

  constructor() {
    this.anthropic = new Anthropic({
      apiKey: ANTHROPIC_API_KEY,
    });
    this.mcp = new Client({ name: "mcp-client-cli", version: "1.0.0" });
  }
  // methods will go here
}

async connectToServer(serverScriptPath: string) {
  try {
    const isJs = serverScriptPath.endsWith(".js");
    const isPy = serverScriptPath.endsWith(".py");
    if (!isJs && !isPy) {
      throw new Error("Server script must be a .js or .py file");
    }
    const command = isPy
      ? process.platform === "win32"
        ? "python"
        : "python3"
      : process.execPath;

    this.transport = new StdioClientTransport({
      command,
      args: [serverScriptPath],
    });
    await this.mcp.connect(this.transport);

    const toolsResult = await this.mcp.listTools();
    this.tools = toolsResult.tools.map((tool) => {
      return {
        name: tool.name,
        description: tool.description,
        input_schema: tool.inputSchema,
      };
    });
    console.log(
      "Connected to server with tools:",
      this.tools.map(({ name }) => name)
    );
  } catch (e) {
    console.log("Failed to connect to MCP server: ", e);
    throw e;
  }
}

async processQuery(query: string) {
  const messages: MessageParam[] = [
    {
      role: "user",
      content: query,
    },
  ];

  const response = await this.anthropic.messages.create({
    model: "claude-opus-5",
    max_tokens: 1000,
    messages,
    tools: this.tools,
  });

  const finalText = [];

  for (const content of response.content) {
    if (content.type === "text") {
      finalText.push(content.text);
    } else if (content.type === "tool_use") {
      const toolName = content.name;
      const toolArgs = content.input as { [x: string]: unknown } | undefined;

      const result = await this.mcp.callTool({
        name: toolName,
        arguments: toolArgs,
      });
      finalText.push(
        `[Calling tool ${toolName} with args ${JSON.stringify(toolArgs)}]`
      );

      messages.push({
        role: "user",
        content: result.content
          .filter((block) => block.type === "text")
          .map((block) => block.text)
          .join("\n"),
      });

      const response = await this.anthropic.messages.create({
        model: "claude-opus-5",
        max_tokens: 1000,
        messages,
      });

      finalText.push(
        response.content[0].type === "text" ? response.content[0].text : ""
      );
    }
  }

  return finalText.join("\n");
}

async chatLoop() {
  const rl = readline.createInterface({
    input: process.stdin,
    output: process.stdout,
  });

  try {
    console.log("\nMCP Client Started!");
    console.log("Type your queries or 'quit' to exit.");

    while (true) {
      const message = await rl.question("\nQuery: ");
      if (message.toLowerCase() === "quit") {
        break;
      }
      const response = await this.processQuery(message);
      console.log("\n" + response);
    }
  } finally {
    rl.close();
  }
}

async cleanup() {
  await this.mcp.close();
}

async function main() {
  if (process.argv.length < 3) {
    console.log("Usage: node index.ts <path_to_server_script>");
    return;
  }
  const mcpClient = new MCPClient();
  try {
    await mcpClient.connectToServer(process.argv[2]);
    await mcpClient.chatLoop();
  } catch (e) {
    console.error("Error:", e);
    await mcpClient.cleanup();
    process.exit(1);
  } finally {
    await mcpClient.cleanup();
    process.exit(0);
  }
}

main();

# Build TypeScript
npm run build

# Run the client
node build/index.js path/to/server.py # python server
node build/index.js path/to/build/index.js # node server

# Relative path
node build/index.js ./server/build/index.js

# Absolute path
node build/index.js /Users/username/projects/mcp-server/build/index.js

# Windows path (either format works)
node build/index.js C:/projects/mcp-server/build/index.js
node build/index.js C:\\projects\\mcp-server\\build\\index.js

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-client</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-model-anthropic</artifactId>
</dependency>

spring:
  ai:
    mcp:
      client:
        enabled: true
        name: brave-search-client
        version: 1.0.0
        type: SYNC
        request-timeout: 20s
        stdio:
          root-change-notification: true
          servers-configuration: classpath:/mcp-servers-config.json
        toolcallback:
          enabled: true
    anthropic:
      api-key: ${ANTHROPIC_API_KEY}

{
  "mcpServers": {
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "<PUT YOUR BRAVE API KEY>"
      }
    }
  }
}

var chatClient = chatClientBuilder
    .defaultSystem("You are useful assistant, expert in AI and Java.")
    .defaultToolCallbacks((Object[]) mcpToolAdapter.toolCallbacks())
    .defaultAdvisors(new MessageChatMemoryAdvisor(new InMemoryChatMemory()))
    .build();

./mvnw clean install
java -jar ./target/ai-mcp-brave-chatbot-0.0.1-SNAPSHOT.jar

./mvnw spring-boot:run

spring.ai.mcp.client.streamable-http.connections.server1.url=http://localhost:8080

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-client-webflux</artifactId>
</dependency>

java --version

./gradlew build

export ANTHROPIC_API_KEY='your-anthropic-api-key-here'

class MCPClient(apiKey: String) : AutoCloseable {
    private val anthropic = AnthropicOkHttpClient.builder()
        .apiKey(apiKey)
        .build()

  private val mcp: Client = Client(
        clientInfo = Implementation(name = "mcp-client-cli", version = "1.0.0")
  )
    private var serverProcess: Process? = null
    private lateinit var tools: List<ToolUnion>

    // methods will go here

    override fun close() {
        runBlocking {
            mcp.close()
        }
        serverProcess?.destroy()
        anthropic.close()
    }
}

suspend fun connectToServer(serverScriptPath: String) {
    val command = buildList {
        when (serverScriptPath.substringAfterLast(".")) {
            "js" -> add("node")
            "py" -> add(if (System.getProperty("os.name").lowercase().contains("win")) "python" else "python3")
            "jar" -> addAll(listOf("java", "-jar"))
            else -> throw IllegalArgumentException("Server script must be a .js, .py or .jar file")
        }
        add(serverScriptPath)
    }

    val process = ProcessBuilder(command).start()
    serverProcess = process

    val transport = StdioClientTransport(
        input = process.inputStream.asSource().buffered(),
        output = process.outputStream.asSink().buffered(),
    )

    mcp.connect(transport)

    val toolsResult = mcp.listTools()
    tools = toolsResult.tools.map { tool ->
        ToolUnion.ofTool(
            Tool.builder()
                .name(tool.name)
                .description(tool.description ?: "")
                .inputSchema(
                    Tool.InputSchema.builder()
                        .type(JsonValue.from(tool.inputSchema.type))
                        .properties(tool.inputSchema.properties?.toJsonValue() ?: EmptyJsonObject.toJsonValue())
                        .putAdditionalProperty("required", JsonValue.from(tool.inputSchema.required))
                        .build(),
                )
                .build(),
        )
    }
    println("Connected to server with tools: ${tools.joinToString(", ") { it.tool().get().name() }}")
}

suspend fun processQuery(query: String): String {
    val messages = mutableListOf(
        MessageParam.builder()
            .role(MessageParam.Role.USER)
            .content(query)
            .build(),
    )

    val response = anthropic.messages().create(
        MessageCreateParams.builder()
            .model("claude-opus-5")
            .maxTokens(1024)
            .messages(messages)
            .tools(tools)
            .build(),
    )

    val finalText = mutableListOf<String>()
    response.content().forEach { content ->
        when {
            content.isText() -> finalText.add(content.text().get().text())

            content.isToolUse() -> {
                val toolName = content.toolUse().get().name()
                val toolArgs =
                    content.toolUse().get()._input().convert(object : TypeReference<Map<String, JsonValue>>() {})

                val result = mcp.callTool(
                    name = toolName,
                    arguments = toolArgs ?: emptyMap(),
                )
                finalText.add("[Calling tool $toolName with args $toolArgs]")

                messages.add(
                    MessageParam.builder()
                        .role(MessageParam.Role.USER)
                        .content(
                            result.content
                                .filterIsInstance<TextContent>()
                                .joinToString("\n") { it.text }
                        )
                        .build(),
                )

                val aiResponse = anthropic.messages().create(
                    MessageCreateParams.builder()
                        .model("claude-opus-5")
                        .maxTokens(1024)
                        .messages(messages)
                        .build(),
                )

                finalText.add(aiResponse.content().first().text().get().text())
            }
        }
    }

    return finalText.joinToString("\n")
}

suspend fun chatLoop() {
    println("\nMCP Client Started!")
    println("Type your queries or 'quit' to exit.")

    while (true) {
        print("\nQuery: ")
        val message = readlnOrNull() ?: break
        if (message.trim().lowercase() == "quit") break

        try {
            val response = processQuery(message)
            println("\n$response")
        } catch (e: Exception) {
            println("\nError: ${e.message}")
        }
    }
}

fun main(args: Array<String>) = runBlocking {
    require(args.isNotEmpty()) { "Usage: java -jar <path> <path_to_server_script>" }

    val apiKey = System.getenv("ANTHROPIC_API_KEY")
    require(!apiKey.isNullOrBlank()) { "ANTHROPIC_API_KEY environment variable is not set" }

    val client = MCPClient(apiKey)
    client.use {
        client.connectToServer(args.first())
        client.chatLoop()
    }
}

./gradlew build

# Run the client
java -jar build/libs/kotlin-mcp-client-0.1.0-all.jar path/to/server.jar # JVM server
java -jar build/libs/kotlin-mcp-client-0.1.0-all.jar path/to/server.py  # Python server
java -jar build/libs/kotlin-mcp-client-0.1.0-all.jar path/to/build/index.js # Node server

./gradlew run --args="path/to/server.jar"

# Relative path
java -jar build/libs/client.jar ./server/build/libs/server.jar

# Absolute path
java -jar build/libs/client.jar /Users/username/projects/mcp-server/build/libs/server.jar

# Windows path (either format works)
java -jar build/libs/client.jar C:/projects/mcp-server/build/libs/server.jar
java -jar build/libs/client.jar C:\\projects\\mcp-server\\build\\libs\\server.jar

dotnet new console -n QuickstartClient
cd QuickstartClient

dotnet add package ModelContextProtocol --prerelease
dotnet add package Anthropic.SDK
dotnet add package Microsoft.Extensions.Hosting
dotnet add package Microsoft.Extensions.AI

dotnet user-secrets init
dotnet user-secrets set "ANTHROPIC_API_KEY" "<your key here>"

using Anthropic.SDK;
using Microsoft.Extensions.AI;
using Microsoft.Extensions.Configuration;
using Microsoft.Extensions.Hosting;
using ModelContextProtocol.Client;
using ModelContextProtocol.Protocol.Transport;

var builder = Host.CreateApplicationBuilder(args);

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

var (command, arguments) = GetCommandAndArguments(args);

var clientTransport = new StdioClientTransport(new()
{
    Name = "Demo Server",
    Command = command,
    Arguments = arguments,
});

await using var mcpClient = await McpClient.CreateAsync(clientTransport);

var tools = await mcpClient.ListToolsAsync();
foreach (var tool in tools)
{
    Console.WriteLine($"Connected to server with tools: {tool.Name}");
}

static (string command, string[] arguments) GetCommandAndArguments(string[] args)
{
    return args switch
    {
        [var script] when script.EndsWith(".py") => ("python", args),
        [var script] when script.EndsWith(".js") => ("node", args),
        [var script] when Directory.Exists(script) || (File.Exists(script) && script.EndsWith(".csproj")) => ("dotnet", ["run", "--project", script, "--no-build"]),
        _ => throw new NotSupportedException("An unsupported server script was provided. Supported scripts are .py, .js, or .csproj")
    };
}

using var anthropicClient = new AnthropicClient(new APIAuthentication(builder.Configuration["ANTHROPIC_API_KEY"]))
    .Messages
    .AsBuilder()
    .UseFunctionInvocation()
    .Build();

var options = new ChatOptions
{
    MaxOutputTokens = 1000,
    ModelId = "claude-opus-5",
    Tools = [.. tools]
};

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

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

    await foreach (var message in anthropicClient.GetStreamingResponseAsync(query, options))
    {
        Console.Write(message);
    }
    Console.WriteLine();

    PromptForInput();
}

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

dotnet run -- path/to/server.csproj # dotnet server
dotnet run -- path/to/server.py # python server
dotnet run -- path/to/server.js # node server

echo "ANTHROPIC_API_KEY=your-api-key-goes-here" > .env

echo ".env" >> .gitignore

require "anthropic"
require "dotenv/load"
require "json"
require "mcp"

class MCPClient
  ANTHROPIC_MODEL = "claude-opus-5"

  def initialize
    @mcp_client = nil
    @transport = nil
    @anthropic_client = nil
  end

  # methods will go here
end

def connect_to_server(server_script_path)
  command = case File.extname(server_script_path)
  when ".rb"
    "ruby"
  when ".py"
    "python3"
  when ".js"
    "node"
  else
    raise ArgumentError, "Server script must be a .rb, .py, or .js file."
  end

  @transport = MCP::Client::Stdio.new(command: command, args: [server_script_path])
  @mcp_client = MCP::Client.new(transport: @transport)
  @mcp_client.connect

  tool_names = @mcp_client.tools.map(&:name)
  puts "\nConnected to server with tools: #{tool_names}"
end

private

def process_query(query)
  messages = [{ role: "user", content: query }]

  available_tools = @mcp_client.tools.map do |tool|
    { name: tool.name, description: tool.description, input_schema: tool.input_schema }
  end

  # Initial Claude API call.
  response = chat(messages, tools: available_tools)

  # Process response and handle tool calls.
  if response.content.any?(Anthropic::Models::ToolUseBlock)
    assistant_content = response.content.filter_map do |content_block|
      case content_block
      when Anthropic::Models::TextBlock
        { type: "text", text: content_block.text }
      when Anthropic::Models::ToolUseBlock
        { type: "tool_use", id: content_block.id, name: content_block.name, input: content_block.input }
      end
    end
    messages << { role: "assistant", content: assistant_content }
  end

  response.content.each_with_object([]) do |content, response_parts|
    case content
    when Anthropic::Models::TextBlock
      response_parts << content.text
    when Anthropic::Models::ToolUseBlock
      # Execute tool call via MCP.
      result = @mcp_client.call_tool(name: content.name, arguments: content.input)
      response_parts << "[Calling tool #{content.name} with args #{content.input.to_json}]"

      tool_result_content = result.dig("result", "content")
      result_text = if tool_result_content.is_a?(Array)
        tool_result_content.filter_map { |content_item| content_item["text"] }.join("\n")
      else
        tool_result_content.to_s
      end

      messages << {
        role: "user",
        content: [{
          type: "tool_result",
          tool_use_id: content.id,
          content: result_text
        }]
      }

      # Get next response from Claude.
      response = chat(messages)

      response.content.each do |content_block|
        response_parts << content_block.text if content_block.is_a?(Anthropic::Models::TextBlock)
      end
    end
  end.join("\n")
end

def chat(messages, tools: nil)
  params = { model: ANTHROPIC_MODEL, max_tokens: 1000, messages: messages }
  params[:tools] = tools if tools

  anthropic_client.messages.create(**params)
end

def anthropic_client
  @anthropic_client ||= Anthropic::Client.new(api_key: ENV["ANTHROPIC_API_KEY"])
end

def chat_loop
  puts <<~MESSAGE
    MCP Client Started!
    Type your queries or 'quit' to exit.
  MESSAGE

  loop do
    print "\nQuery: "
    line = $stdin.gets
    break if line.nil?

    query = line.chomp.strip
    break if query.downcase == "quit"
    next if query.empty?

    begin
      response = process_query(query)
      puts "\n#{response}"
    rescue => e
      puts "\nError: #{e.message}"
    end
  end
end

def cleanup
  @transport&.close
end

if ARGV.empty?
  puts "Usage: ruby client.rb <path_to_server_script>"
  exit 1
end

client = MCPClient.new

begin
  client.connect_to_server(ARGV[0])

  api_key = ENV["ANTHROPIC_API_KEY"]
  if api_key.nil? || api_key.empty?
    puts <<~MESSAGE
      No ANTHROPIC_API_KEY found. To query these tools with Claude, set your API key:
        export ANTHROPIC_API_KEY=your-api-key-here
    MESSAGE
    exit
  end

  client.chat_loop
rescue => e
  puts "Error: #{e.message}"
  exit 1
ensure
  client.cleanup
end

bundle exec ruby client.rb path/to/server.rb # ruby server
bundle exec ruby client.rb path/to/server.py # python server
bundle exec ruby client.rb path/to/build/index.js # node server

# Relative path
bundle exec ruby client.rb ./server/weather.rb

# Absolute path
bundle exec ruby client.rb /Users/username/projects/mcp-server/weather.rb

# Windows path (either format works)
bundle exec ruby client.rb C:/projects/mcp-server/weather.rb
bundle exec ruby client.rb C:\\projects\\mcp-server\\weather.rb

cargo new mcp-client-rust
cd mcp-client-rust

echo "ANTHROPIC_API_KEY=your-api-key-goes-here" > .env

echo ".env" >> .gitignore

use anyhow::{Context, Result, bail};
use genai::Client;
use genai::chat::{
    ChatMessage, ChatRequest, ChatResponse, ContentPart, Tool as GenaiTool, ToolResponse,
};
use rmcp::model::{CallToolRequestParam, Tool as McpTool};
use rmcp::service::{RoleClient, RunningService, ServiceExt};
use rmcp::transport::TokioChildProcess;
use serde_json::Value;
use tokio::io::{self, AsyncBufReadExt, BufReader};
use tokio::process::Command;

const MODEL_ANTHROPIC: &str = "claude-opus-5";

struct MCPClient {
    anthropic: Client,
    session: Option<RunningService<RoleClient, ()>>,
    tools: Vec<GenaiTool>,
}

impl MCPClient {
    fn new() -> Result<Self> {
        Ok(MCPClient {
            anthropic: Client::default(),
            session: None,
            tools: Vec::new(),
        })
    }

    // Additional methods will go here.
}

async fn connect_to_server(&mut self, server_args: &[String]) -> Result<()> {
    if self.session.is_some() {
        bail!("Client is already connected to a server");
    }

    let mut command = Command::new(&server_args[0]);
    command.args(&server_args[1..]);

    let process = TokioChildProcess::new(command)
        .with_context(|| format!("Failed to spawn server process for {:?}", server_args))?;

    let session = ().serve(process).await?;

    let rmcp_tools = session
        .list_all_tools()
        .await
        .context("Unable to list tools from server")?;

    let tool_names: Vec<String> = rmcp_tools
        .iter()
        .map(|tool| tool.name.to_string())
        .collect();

    println!("Connected to server with tools: {tool_names:?}");

    self.tools = convert_tools(&rmcp_tools);
    self.session = Some(session);
    Ok(())
}

fn convert_tools(tools: &[McpTool]) -> Vec<GenaiTool> {
    tools
        .iter()
        .map(|tool| GenaiTool {
            name: tool.name.to_string(),
            description: tool.description.as_deref().map(str::to_string),
            schema: Some(Value::Object(tool.input_schema.as_ref().clone())),
            config: None,
        })
        .collect()
}

async fn request_model(&self, chat_req: &ChatRequest) -> Result<ChatResponse> {
    let response = self
        .anthropic
        .exec_chat(MODEL_ANTHROPIC, chat_req.clone(), None)
        .await
        .context("Anthropic chat request failed")?;

    Ok(response)
}

async fn process_query(&mut self, query: &str) -> Result<String> {
    let session = self
        .session
        .as_ref()
        .context("Client is not connected to any server")?;

    let mut messages = vec![ChatMessage::user(query)];
    let mut final_text = Vec::new();

    // Initial Claude API call with tools
    let mut chat_req = ChatRequest::new(messages.clone()).with_tools(self.tools.clone());
    let mut chat_rsp = self.request_model(&chat_req).await?;

    // Process response content - collect text and handle tool calls
    for text in chat_rsp.texts() {
        final_text.push(text.to_string());
    }

    let tool_calls = chat_rsp.tool_calls();
    if !tool_calls.is_empty() {
        // Append assistant's response to message history
        messages.push(ChatMessage::assistant(chat_rsp.content.clone()));

        // Execute each tool call and collect responses
        let mut tool_results = Vec::new();
        for tool_call in tool_calls {
            // Add information about the tool call to final text
            let tool_args_str = serde_json::to_string(&tool_call.fn_arguments)
                .unwrap_or_else(|_| "{}".to_string());

            final_text.push(format!(
                "[Calling tool {} with args {}]",
                tool_call.fn_name, tool_args_str
            ));

            // Query the MCP server
            let tool_result = session
                .call_tool(CallToolRequestParam {
                    name: tool_call.fn_name.clone().into(),
                    arguments: tool_call.fn_arguments.as_object().cloned(),
                })
                .await
                .with_context(|| format!("Tool call {} failed", tool_call.fn_name))?;

            let payload = serde_json::to_string(&tool_result)
                .context("Failed to serialize tool result")?;

            tool_results.push(ContentPart::ToolResponse(ToolResponse::new(
                tool_call.call_id.clone(),
                payload,
            )));
        }

        // Append tool responses to message history
        messages.push(ChatMessage::user(tool_results));

        // Build the next request and query model
        chat_req = ChatRequest::new(messages.clone());
        chat_rsp = self.request_model(&chat_req).await?;

        // Collect text from response
        for text in chat_rsp.texts() {
            final_text.push(text.to_string());
        }
    }

    Ok(final_text.join("\n"))
}