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Spring-AI 结合自定义 mcp server 实现飞书智能机器人

分享 / 0 / 0 / 创建于 6小时前

  1. 简介
    本文主要介绍基于 spring ai 自定义搭建 mcp 服务端和客户端，主要场景是：

基于本地的mcp服务让飞书机器人跟场景回答不同的问题

实现效果如下

最后附了源码链接.整体代码非常简单，容易上手。

2.概念
2.1 什么是 AI MCP？
MCP（Model Context Protocol） 是一种 ​标准化协议​，用来解决一个长期痛点：

👉 如何让 AI“安全、可控、标准化地”调用外部工具 / 系统能力？

它的核心目标不是“让 AI 更聪明”，而是：

让 AI 能调用真实系统
同时 避免 AI 乱编、乱连、乱访问
并且 工具接入方式统一
2.2 MCP 解决了什么问题？
1️⃣ 传统 Function Call 的问题
以 OpenAI Function Calling / Tool Calling 为例：

工具定义写死在 Prompt 或代码里
每个 AI 框架一套接口
权限 / 生命周期 / 连接管理全靠业务自己写
工具多了以后 极难维护
👉 在真实系统里会变成：

Prompt 很长
Tool 定义重复
不同 AI 模型不可复用
2️⃣ MCP 的解决思路
MCP 把 ​工具变成一个标准化的 Server​：

AI Model
|
| MCP 协议
|
MCP Client ────── MCP Server
├─ 查数据库
├─ 调内部系统
├─ 查文件
├─ 调 HTTP API
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AI ​不直接接触工具实现​，只通过 MCP 协议：

发现工具
调用工具
获取结构化结果

  1. 工程结构
    主要分为2个模块，mcp-server 和 mcp-client, mcp-client里面通过 java -jar 的形式运行 mcp 服务端，在 mcp-client 最终结合 AI 以及飞书机器人的集成实现消息的回复.
  1. 相关源码介绍
  2. 1 mcp 服务端
    服务端非常简单，例如保留一个天气的服务，只需要在方法上加入org.springframework.ai.tool.annotation.Tool; 注解即可

import io.kings1990.mcp.mcpserver.enums.WeatherType;
import io.kings1990.mcp.mcpserver.record.WeatherRequest;
import io.kings1990.mcp.mcpserver.record.WeatherResult;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.tool.annotation.Tool;
import org.springframework.ai.tool.annotation.ToolParam;
import org.springframework.stereotype.Service;

@Service
@Slf4j
public class WeatherService {

@Tool(name = "getWeather", description = "查询指定城市的天气")
public WeatherResult getWeather(@ToolParam(description = "请求参数") WeatherRequest req) {
    log.info("MCP Tool getWeather called, city={}", req.city());
    return new WeatherResult(
            req.city(),
            WeatherType.SUNNY,
            "25°C",
            "°C",
            "mcp:getWeather"
    );
}

}
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4.2 mcp客户端
4.2.1让 AI 集成 ToolCallbacks
@Configuration
public class AiConfig {

@Bean
public ChatClient chatClient(ChatClient.Builder builder, List<McpSyncClient> mcpSyncClients) {
    return builder
            .defaultSystem("你是一个AI助手，必须调用工具 kings-spring-ai-mcp-tools 下的方法，如果工具不可用，就明确说明无法调用工具，不要编造。")
            .defaultToolCallbacks(
                    SyncMcpToolCallbackProvider.builder()
                            .mcpClients(mcpSyncClients)
                            .build()
            )
            .build();
}

}
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4.2.2基于飞书机器人的长链接集成，实现消息的自动回复
import cn.hutool.core.thread.ThreadUtil;
import com.lark.oapi.event.EventDispatcher;
import com.lark.oapi.service.im.ImService;
import com.lark.oapi.service.im.v1.model.P2MessageReceiveV1;
import com.lark.oapi.ws.Client;
import jakarta.annotation.Resource;
import org.springframework.beans.factory.DisposableBean;
import org.springframework.boot.CommandLineRunner;
import org.springframework.stereotype.Component;

@Component
public class LarkWsListener implements CommandLineRunner, DisposableBean {

@Resource
private LarkBotService botService;

@Resource
private Client.Builder larkWsBuilder;

@Override
public void run(String... args) {
    //verificationToken和 encryptionKey 可选，用于验证和解密事件
    EventDispatcher handler = EventDispatcher.newBuilder("", "")
            .onP2MessageReceiveV1(new ImService.P2MessageReceiveV1Handler() {
                @Override
                public void handle(P2MessageReceiveV1 event) throws Exception {

                    // 1) messageId 用于 reply
                    String messageId = event.getEvent().getMessage().getMessageId();

                    // 2) content 是 JSON 字符串，需要解析出文本
                    String contentJson = event.getEvent().getMessage().getContent();

                    System.err.println("收到消息: " + contentJson);

                    String userText = LarkMsgParser.extractText(contentJson);

                    ThreadUtil.execAsync(() -> {
                        botService.onUserMessage(messageId, userText);
                    });

                }
            })
            .build();

    // 建议把 appId/appSecret 放配置文件
    Client wsClient = larkWsBuilder.eventHandler(handler).build();

    wsClient.start();
}


@Override
public void destroy() throws Exception {

}

}
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4.2.3 AI Api-Key 植入
我这边使用 zhipu ai. 这边可以获取 api-key.

加入依赖

org.springframework.ai spring-ai-starter-model-zhipuai 1. 2. 3. 4. 并且在配置中配置 api-key

spring:
ai:
zhipuai:
api-key: your_api_key_here
chat:
options:
model: glm-4.6
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5.启动
直接运行mcp-client 主程序，查看飞书机器人是否注册成功

connected to wss://msg-frontier.feishu.cn/
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启动成功后在飞书应用里输入例如北京，等待机器人回复
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6.源码
github 仓库. 可以 star 查看后续更新

Fast Request是一个类似于 Postman 的 IDEA 插件。它是一个强大的 restful api 工具包插件，可以根据已有的方法帮助您快速生成 url 和 params。 Restful Fast Request = API调试工具 + API管理工具 + API搜索工具。 它有一个漂亮的界面来完成请求、检查服务器响应、存储你的 api 请求和导出 api 请求。插件帮助你在 IDEA 界面内更快更高效得调试你的 API。

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