MCP 模型上下文协议

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("support-tools")

@mcp.tool()
def query_order(order_id: str) -> str:
    """查询订单状态。order_id 形如 SO-12345。"""
    return orders.get(order_id, f"未找到订单 {order_id}，请确认单号格式")

@mcp.tool()
def create_ticket(title: str, priority: str = "normal") -> str:
    """创建客服工单。priority 可选 low / normal / high。"""
    return f"工单已创建：{title}（优先级 {priority}）"

@mcp.resource("config://app")
def app_config() -> str:
    """把应用配置作为只读资料暴露给宿主"""
    return json.dumps({"channels": ["电话", "邮件"], "sla_hours": 24})

@mcp.prompt()
def triage(issue: str) -> str:
    """工单分诊模板：生成一段结构化分析提示"""
    return (f"用户报告了问题：{issue}。\n"
            "请按影响面、紧急度、建议动作三段输出。")

if __name__ == "__main__":
    mcp.run()          # 默认 stdio 传输

# 假设 server 写在 server.py，用 uv 管依赖
mcp dev server.py            # 拉起 Inspector，自动连接你的 server

# 或者直接跑 Node 版 Inspector 连任意 server
npx @modelcontextprotocol/inspector uv run server.py

{
  "mcpServers": {
    "support-tools": {
      "command": "uv",
      "args": ["run", "--directory", "E:/yr/ai/support-mcp", "server.py"]
    },
    "web-search": {
      "url": "https://mcp.example.com/mcp"
    }
  }
}

from langchain.chat_models import init_chat_model
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_agent

model = init_chat_model("openai:gpt-4o")

client = MultiServerMCPClient({
    "support": {                          # 本地 server：stdio
        "transport": "stdio",
        "command": "uv",
        "args": ["run", "--directory", "E:/yr/ai/support-mcp", "server.py"],
    },
    "search": {                           # 远程 server：Streamable HTTP
        "transport": "streamable_http",
        "url": "https://mcp.example.com/mcp",
    },
})

tools = await client.get_tools()          # MCP tools 变成 LangChain tools
agent = create_agent(model, tools)        # 第一章的 agent，一个字不改

result = await agent.ainvoke(
    {"messages": [("user", "查一下订单 SO-1024 的状态，如果是待发货就建个高优工单")]}
)