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构建你的 AI 模块宇宙：Spring AI MCP Server 深度定制指南

news 来源：原创 2025/6/25 7:14:05

引言：当模块化遇见 AI

在微服务架构的海洋中，MCP（Module Communication Protocol）就像一艘智能帆船，它让不同 AI 模块的通信变得优雅而高效。本文将带你构建一艘属于自己的 AI 智能帆船——自定义 Spring AI MCP Server。通过模块化设计模式、动态路由策略和AI 模型热插拔三大核心思想，我们将创建一个可扩展、可演进的 AI 服务基础设施。

一、构建基石：环境准备

1.1 技术栈选型

Spring Boot 3.1：支持 JDK 17+，性能提升 20%
Spring AI 1.0.0-M6：MCP 协议核心实现
Micrometer + Prometheus：实时监控 AI 调用指标
Docker + Kubernetes：容器化部署方案

<!-- pom.xml关键依赖 -->
<dependencies><!-- Spring Boot Web --><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><!-- Spring AI MCP Server --><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-mcp-server-webmvc-spring-boot-starter</artifactId><version>1.0.0-M6</version></dependency><!-- 微服务治理 --><dependency><groupId>org.springframework.cloud</groupId><artifactId>spring-cloud-starter</artifactId></dependency>
</dependencies>

二、创新架构设计

2.1 模块化分层架构

2.2 核心组件设计

McpRouter：基于Reactive RouterFunction实现的智能路由
ModelRegistry：支持动态注册/注销 AI 模型的注册中心
ContextManager：上下文管理器，支持多租户隔离
MetricsCollector：集成 Prometheus 的指标收集器

三、实战演练：构建天气预报 AI 服务

3.1 定义服务接口

@McpService(name = "weather-service", version = "1.0")
public interface WeatherService {@McpOperation(description = "获取城市天气", inputType = String.class)String getWeather(String city);
}

3.2 实现智能服务

@Service
public class WeatherServiceImpl implements WeatherService {private final AiModelClient aiModelClient;public WeatherServiceImpl(AiModelClient aiModelClient) {this.aiModelClient = aiModelClient;}@Overridepublic String getWeather(String city) {// 动态选择模型：Qwen vs LlamaString model = ModelSelector.select(city);return aiModelClient.invoke(model,Map.of("city", city, "temperature_unit", "Celsius"));}
}

3.3 模型热插拔实现

@Component
public class ModelSelector {@Value("${ai.models.weather}")private List<String> weatherModels;public String select(String city) {// 基于地理位置选择最佳模型if (city.startsWith("Shanghai")) {return "qwen-max";} else {return "llama-3";}}
}

四、高级特性实现

4.1 动态路由策略

@Configuration
public class McpRouterConfig {@Beanpublic RouterFunction<ServerResponse> mcpRoutes(WeatherService weatherService) {return RouterFunctions.route(RequestPredicates.POST("/mcp/weather").and(RequestPredicates.contentType(MediaType.APPLICATION_JSON)),request -> ServerResponse.ok().body(ValueOps.of(weatherService.getWeather(request.body()))));}
}

4.2 上下文感知设计

@Component
public class RequestContextFilter implements Filter {@Overridepublic void doFilter(ServletRequest request, ServletResponse response, FilterChain chain) {try {// 提取上下文信息String tenantId = request.getParameter("tenant");// 设置线程局部变量ContextManager.setCurrentContext(Context.builder().tenantId(tenantId).modelVersion("v1.2").build());chain.doFilter(request, response);} finally {ContextManager.clearCurrentContext();}}
}

五、监控与可观测性

5.1 Prometheus 指标配置

management:metrics:tags:application: mcp-serverexport:prometheus:enabled: true

5.2 自定义指标示例

@Component
public class AiCallMetrics {private final Counter aiCalls = Metrics.counter("ai.calls.total");public void recordCall(String model, Duration duration) {aiCalls.tag("model", model).increment();Metrics.timer("ai.call.duration").tag("model", model).record(duration);}
}

六、部署与扩展

6.1 Docker 部署方案

FROM openjdk:17-jdk-slim
VOLUME /tmp
ARG JAR_FILE=target/mcp-server-0.0.1-SNAPSHOT.jar
COPY ${JAR_FILE} app.jar
ENTRYPOINT ["java", "-jar", "-Dspring.profiles.active=prod", "app.jar"]

6.2 Kubernetes 部署示例

apiVersion: apps/v1
kind: Deployment
metadata:name: mcp-server
spec:replicas: 3selector:matchLabels:app: mcp-servertemplate:metadata:labels:app: mcp-serverspec:containers:- name: mcp-serverimage: your-registry/mcp-server:latestports:- containerPort: 8080envFrom:- configMapRef:name: mcp-config