fastmcp构建mcp server和client

文章目录

项目官网

fastmcp

Server

mcp_server.py

python 复制代码
# 新建实例,这个一定要是一个单独的文件
from fastmcp import FastMCP

mcp = FastMCP(name="MyAssistantServer")

weather_tools.py

python 复制代码
# 工具集需要导入之前的server实列
from typing import Annotated
from mcp_server import mcp
import datetime
from dataclasses import dataclass, field

@dataclass
class WeatherResult:
    date: datetime.date=field(metadata={"description": "日期"})
    temperature: float=field(metadata={"description": "温度,摄氏度"})
    weather:str=field(metadata={"description": "天气:小雨,晴,阴,雾等等"})

@dataclass
class CityWeahterResult:
    city: str=field(metadata={"description": "城市"})
    results: list[WeatherResult]=field(metadata={"description": "几天的天气集合"})

@mcp.tool(
        name="Get city weather",
        description="获取某个城市未来几天的天气",)
def get_weather(
    city: Annotated[str,"城市名"],
    days:Annotated[int,"想要预测的天数"]) -> CityWeahterResult:
    return CityWeahterResult()

run_server.py

python 复制代码
from mcp_server import mcp
import weather_tools #只要导入的工具集,就会自动注册

if __name__ == "__main__":
    mcp.run(transport="http",host="127.0.0.1", port=8765)

客户端

client.py

python 复制代码
import asyncio
from fastmcp import Client


client = Client("http://127.0.0.1:8765/mcp")

async def main():
    async with client:
        # Basic server interaction
        await client.ping()
        
        # List available operations
        tools = await client.list_tools()
        resources = await client.list_resources()
        prompts = await client.list_prompts()
        
        # Execute operations
        print(tools)

asyncio.run(main())

扫描到的工具集

javascript 复制代码
[
  Tool(
    (name = "Get city weather"),
    (title = None),
    (description = "获取某个城市未来几天的天气"),
    (inputSchema = {
      properties: {
        city: { description: "城市名", type: "string" },
        days: { description: "想要预测的天数", type: "integer" },
      },
      required: ["city", "days"],
      type: "object",
    }),
    (outputSchema = {
      $defs: {
        WeatherResult: {
          properties: {
            date: { description: "日期", format: "date", type: "string" },
            temperature: { description: "温度,摄氏度", type: "number" },
            weather: {
              description: "天气:小雨,晴,阴,雾等等",
              type: "string",
            },
          },
          required: ["date", "temperature", "weather"],
          type: "object",
        },
      },
      properties: {
        city: { description: "城市", type: "string" },
        results: {
          description: "几天的天气集合",
          items: { $ref: "#/$defs/WeatherResult" },
          type: "array",
        },
      },
      required: ["city", "results"],
      type: "object",
    }),
    (icons = None),
    (annotations = None),
    (meta = { _fastmcp: { tags: [] } }),
    (execution = None)
  ),
];
相关推荐
带刺的坐椅7 小时前
从 Claude Code 隐私争议,看 SolonCode 的设计选择
ai·llm·agent·claudecode·soloncode·codingplan
金銀銅鐵7 小时前
[Python] 基于欧几里得算法,实现分数约分计算器
python·数学
Lyn_Li9 小时前
Kaggle Top 5 | 198只股票、200条数据的金融预测——BattleFin高分方案从零复现
python·kaggle·比赛复盘·金融预测
lincats11 小时前
Claude Code项目越写越乱?这套清理流程能救你
ai·ai agent·claude code
Java陈序员12 小时前
企业级!一个基于 Java 开发的开源 AI 应用开发平台!
spring boot·agent·mcp
小九九的爸爸13 小时前
前端想要入门Agent开发,要具备哪些Python基础?
python·agent·ai编程
阿耶同学14 小时前
手把手教你用 LangGraph 搭建三层嵌套 Agent 架构
python·程序员
云燕实验室CloudLab15 小时前
《AI开始"抱团"思考了!多智能体 + 思维图到底有多强?》
ai·学习工具·智慧学伴
小七-七牛开发者15 小时前
论文解读:DeepSeek DSpark 在真实高并发推理服务中,如何保证 Token 生成又好又快?
ai·大模型·编程·ai coding
Flynt20 小时前
接手28万行遗留代码:我用codebase-memory-mcp把代码理解时间从3天压到2小时
ai编程·claude·mcp