{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dede82b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 安装依赖:uv pip install langchain_mcp_adapters langchain-deepseek -i https://pypi.tuna.tsinghua.edu.cn/simple\n",
    "\n",
    "from langchain_mcp_adapters.client import MultiServerMCPClient\n",
    "\n",
    "# MultiServerMCPClient 可以同时挂多个 MCP Server，给每个 Server 起个名字即可\n",
    "mcp_client = MultiServerMCPClient({\n",
    "    \"amap\": {\n",
    "      \"transport\": \"sse\",\n",
    "      \"url\": \"https://mcp.api-inference.modelscope.net/6ae8935632f641/sse\"\n",
    "    },\n",
    "    \"mcp_tools\": {\n",
    "        \"url\": \"http://localhost:8000/sse\",   # 指向上面启动的 FastMCP 服务\n",
    "        \"transport\": \"sse\",\n",
    "    },\n",
    "  })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e1f6c411",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前可用工具：['maps_regeocode', 'maps_geo', 'maps_ip_location', 'maps_weather', 'maps_search_detail', 'maps_bicycling', 'maps_direction_walking', 'maps_direction_driving', 'maps_direction_transit_integrated', 'maps_distance', 'maps_text_search', 'maps_around_search']\n"
     ]
    }
   ],
   "source": [
    "#获取工具\n",
    "tools = await mcp_client.get_tools()\n",
    "print(f\"当前可用工具：{[t.name for t in tools]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a6f98a4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_deepseek import ChatDeepSeek\n",
    "from langchain.agents import create_agent\n",
    "from langchain_core.prompts import ChatPromptTemplate\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "load_dotenv()\n",
    "\n",
    "api_key = os.getenv('OPENAI_API_KEY')\n",
    "\n",
    "# 使用 DeepSeek 作为底座模型（国内访问快、性价比高）\n",
    "llm = ChatDeepSeek(\n",
    "    model=\"deepseek-chat\",\n",
    "    api_key=api_key,  # 替换为你自己的 API Key\n",
    ")\n",
    "\n",
    "async def ask_agent(question: str) -> str:\n",
    "    \"\"\"\n",
    "    核心调用函数：从 MCP Server 拉取工具 → 注入 Agent → 执行对话。\n",
    "\n",
    "    每次调用都会重新拉取工具列表，保证服务端的工具变更能实时生效。\n",
    "    \"\"\"\n",
    "    # 第一步：向所有已配置的 MCP Server 拉取可用工具\n",
    "    tools = await mcp_client.get_tools()\n",
    "\n",
    "    # 第二步：用工具 + 模型 + 提示词组装 Agent\n",
    "    agent = create_agent(\n",
    "        model=llm,\n",
    "        tools=tools,\n",
    "        system_prompt=\"你是一个出行助手，可以使用地图工具帮用户查天气、规划路线。回答要简洁，不要编造数据。\",\n",
    "    )\n",
    "\n",
    "    # 第三步：执行对话，让 LLM 自行判断是否需要调用工具\n",
    "    result = await agent.ainvoke({\n",
    "        \"messages\": [{\"role\": \"user\", \"content\": question}]\n",
    "    })\n",
    "\n",
    "    # 第四步：返回最后一条消息（即 LLM 的最终回复）\n",
    "    return result[\"messages\"][-1].content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e81fc28",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "\n",
    "async def main():\n",
    "    # 查天气——LLM 会自动调用 amap 的天气工具\n",
    "    # weather = await ask_agent(\"成都今天天气怎么样？\")\n",
    "    # print(f\"天气查询结果：{weather}\\n\")\n",
    "\n",
    "    # # 规划路线——不同的问题，LLM 会选择不同的工具\n",
    "    # route = await ask_agent(\"帮我规划一条从春熙路到双流机场的地铁换乘路线\")\n",
    "    # print(f\"路线规划结果：{route}\")\n",
    "\n",
    "    route = await ask_agent(\"帮助我计算 bmi，我的体重是 100kg，身高 192cm\")\n",
    "    print(f\"路线规划结果：{route}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38a6ab86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "路线规划结果：你的 BMI 计算结果如下：\n",
      "\n",
      "- **BMI 指数**：27.1\n",
      "- **健康评级**：**偏胖**\n",
      "- **建议**：注意饮食，可以适当增加运动哦 🏃\n",
      "\n",
      "BMI 在 18.5~24 之间属于正常范围，你目前略高一些，保持健康生活习惯就好！还有其他需要帮忙的吗？\n"
     ]
    }
   ],
   "source": [
    "await main()"
   ]
  }
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