Model Context Protocol (MCP) 快速开始

async def start(self):
    # 初始化所有的 mcp server
    for server in self.servers:
        await server.initialize()

    # 获取所有的 tools 命名为 all_tools
    all_tools = []
    for server in self.servers:
        tools = await server.list_tools()
        all_tools.extend(tools)

    # 将所有的 tools 的功能描述格式化成字符串供 LLM 使用
    # tool.format_for_llm() 我放到了这段代码最后，方便阅读。
    tools_description = "\n".join(
        [tool.format_for_llm() for tool in all_tools]
    )

    # 基于 prompt 和当前所有工具的信息，询问 LLM（Claude）应该使用哪些工具。
    system_message = (
        "You are a helpful assistant with access to these tools:\n\n"
        f"{tools_description}\n"
        "Choose the appropriate tool based on the user's question. "
        "If no tool is needed, reply directly.\n\n"
        "IMPORTANT: When you need to use a tool, you must ONLY respond with "
        "the exact JSON object format below, nothing else:\n"
        "{\n"
        '    "tool": "tool-name",\n'
        '    "arguments": {\n'
        '        "argument-name": "value"\n'
        "    }\n"
        "}\n\n"
        "After receiving a tool's response:\n"
        "1. Transform the raw data into a natural, conversational response\n"
        "2. Keep responses concise but informative\n"
        "3. Focus on the most relevant information\n"
        "4. Use appropriate context from the user's question\n"
        "5. Avoid simply repeating the raw data\n\n"
        "Please use only the tools that are explicitly defined above."
    )
    messages = [{"role": "system", "content": system_message}]

    while True:
        # Final... 假设这里已经处理了用户消息输入.
        messages.append({"role": "user", "content": user_input})

        # 将 system_message 和用户消息输入一起发送给 LLM
        llm_response = self.llm_client.get_response(messages)


class Tool:
    """Represents a tool with its properties and formatting."""

    def __init__(
        self, name: str, description: str, input_schema: dict[str, Any]
    ) -> None:
        self.name: str = name
        self.description: str = description
        self.input_schema: dict[str, Any] = input_schema

    # 把工具的名字 / 工具的描述（description）和工具所需要的参数（args_desc）转化为文本
    def format_for_llm(self) -> str:
        """Format tool information for LLM.

        Returns:
            A formatted string describing the tool.
        """
        args_desc = []
        if "properties" in self.input_schema:
            for param_name, param_info in self.input_schema["properties"].items():
                arg_desc = (
                    f"- {param_name}: {param_info.get('description', 'No description')}"
                )
                if param_name in self.input_schema.get("required", []):
                    arg_desc += " (required)"
                args_desc.append(arg_desc)

        return f"""
Tool: {self.name}
Description: {self.description}
Arguments:
{chr(10).join(args_desc)}
"""

server.registerTool(
  'web_browser',
  '浏览网页并获取内容。用于查询最新信息、搜索网络内容或访问特定网站。',
  {
    url: {
      type: 'string',
      description: '要访问的网页URL',
      required: true,
    },
    element_selector: {
      type: 'string',
      description: '可选的CSS选择器，用于提取特定元素',
      required: false,
    },
  },
  (url, elementSelector) => webBrowserImplementation(url, elementSelector)
)

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "<YOUR_GITHUB_PERSONAL_ACCESS_TOKEN>"
      }
    }
  }
}