为客户端开发者

# 创建项目目录
uv init mcp-client
cd mcp-client

# 创建虚拟环境
uv venv

# 激活虚拟环境
# 在 Windows 上：
.venv\Scripts\activate
# 在 Unix 或 MacOS 上：
source .venv/bin/activate

# 安装所需的包
uv add mcp anthropic python-dotenv

# 删除样板文件
rm hello.py

# 创建我们的主文件
touch client.py

# 创建 .env 文件
touch .env

ANTHROPIC_API_KEY=<your key here>

echo ".env" >> .gitignore

import asyncio
from typing import Optional
from contextlib import AsyncExitStack

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

from anthropic import Anthropic
from dotenv import load_dotenv

load_dotenv()  # 从 .env 加载环境变量

class MCPClient:
    def __init__(self):
        # 初始化会话和客户端对象
        self.session: Optional[ClientSession] = None
        self.exit_stack = AsyncExitStack()
        self.anthropic = Anthropic()
    # 方法将在这里添加

async def connect_to_server(self, server_script_path: str):
    """连接到 MCP 服务器

    参数：
        server_script_path: 服务器脚本的路径（.py 或 .js）
    """
    is_python = server_script_path.endswith('.py')
    is_js = server_script_path.endswith('.js')
    if not (is_python or is_js):
        raise ValueError("服务器脚本必须是 .py 或 .js 文件")

    command = "python" if is_python else "node"
    server_params = StdioServerParameters(
        command=command,
        args=[server_script_path],
        env=None
    )

    stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))
    self.stdio, self.write = stdio_transport
    self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))

    await self.session.initialize()

    # 列出可用工具
    response = await self.session.list_tools()
    tools = response.tools
    print("\n已连接到服务器，工具有：", [tool.name for tool in tools])

async def process_query(self, query: str) -> str:
    """使用 Claude 和可用工具处理查询"""
    messages = [
        {
            "role": "user",
            "content": query
        }
    ]

    response = await self.session.list_tools()
    available_tools = [{
        "name": tool.name,
        "description": tool.description,
        "input_schema": tool.inputSchema
    } for tool in response.tools]

    # 初始 Claude API 调用
    response = self.anthropic.messages.create(
        model="claude-3-5-sonnet-20241022",
        max_tokens=1000,
        messages=messages,
        tools=available_tools
    )

    # 处理响应并处理工具调用
    tool_results = []
    final_text = []

    assistant_message_content = []
    for content in response.content:
        if content.type == 'text':
            final_text.append(content.text)
            assistant_message_content.append(content)
        elif content.type == 'tool_use':
            tool_name = content.name
            tool_args = content.input

            # 执行工具调用
            result = await self.session.call_tool(tool_name, tool_args)
            tool_results.append({"call": tool_name, "result": result})
            final_text.append(f"[调用工具 {tool_name}，参数 {tool_args}]")

            assistant_message_content.append(content)
            messages.append({
                "role": "assistant",
                "content": assistant_message_content
            })
            messages.append({
                "role": "user",
                "content": [
                    {
                        "type": "tool_result",
                        "tool_use_id": content.id,
                        "content": result.content
                    }
                ]
            })

            # 从 Claude 获取下一个响应
            response = self.anthropic.messages.create(
                model="claude-3-5-sonnet-20241022",
                max_tokens=1000,
                messages=messages,
            )

            final_text.append(response.content[0].text)

    return "\n".join(final_text)

async def chat_loop(self):
    """运行交互式聊天循环"""
    print("\nMCP 客户端已启动！")
    print("输入您的查询或输入 'quit' 退出。")

    while True:
        try:
            query = input("\n查询：").strip()

            if query.lower() == 'quit':
                break

            response = await self.process_query(query)
            print("\n" + response)

        except Exception as e:
            print(f"\n错误：{str(e)}")

async def cleanup(self):
    """清理资源"""
    await self.exit_stack.aclose()

async def main():
    if len(sys.argv) < 2:
        print("用法：python client.py <服务器脚本路径>")
        sys.exit(1)

    client = MCPClient()
    try:
        await client.connect_to_server(sys.argv[1])
        await client.chat_loop()
    finally:
        await client.cleanup()

if __name__ == "__main__":
    import sys
    asyncio.run(main())

uv run client.py path/to/server.py # python server
uv run client.py path/to/build/index.js # node server

# 相对路径
uv run client.py ./server/weather.py

# 绝对路径
uv run client.py /Users/username/projects/mcp-server/weather.py

# Windows 路径（任一格式均可）
uv run client.py C:/projects/mcp-server/weather.py
uv run client.py C:\projects\mcp-server\weather.py

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-mcp-client-spring-boot-starter</artifactId>
</dependency>
<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-anthropic-spring-boot-starter</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
    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.")
    .defaultTools((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

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