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Spring AI 实现 STDIO和SSE MCP Server
Java MCP 三层架构中,传输的方式有STDIO和SSE两种,如下图所示。
STDIO方式是基于进程间通信,MCP Client和MCP Server运行在同一主机,主要用于本地集成、命令行工具等场景。
SSE方式是基于HTTP协议,MCP Client远程调用MCP Server提供的SSE服务。实现客户端和服务端远程通信。
SSE Server
spring-ai-starter-mcp-server-webflux
基于WebFlux SSE 实现SSE Server。
<dependency>
<groupId>org.springframework.ai</groupId>
<artifactId>spring-ai-starter-mcp-server-webflux</artifactId>
</dependency>
MCP 服务端功能支持基于 Spring WebFlux 的 SSE(服务器发送事件)服务器传输和可选的 STDIO 传输。
1.新建Spring Boot项目
使用https://start.spring.io/新建项目,引入以下依赖。
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.4.4</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.mcp.example</groupId><artifactId>mcp-webflux-server-example</artifactId><version>0.0.1-SNAPSHOT</version><name>mcp-webflux-server-example</name><description>mcp-webflux-server-example</description><dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>1.0.0-SNAPSHOT</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-mcp-server-webflux</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId></dependency></dependencies><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build><repositories><repository><name>Central Portal Snapshots</name><id>central-portal-snapshots</id><url>https://central.sonatype.com/repository/maven-snapshots/</url><releases><enabled>false</enabled></releases><snapshots><enabled>true</enabled></snapshots></repository><repository><id>spring-milestones</id><name>Spring Milestones</name><url>https://repo.spring.io/milestone</url><snapshots><enabled>false</enabled></snapshots></repository><repository><id>spring-snapshots</id><name>Spring Snapshots</name><url>https://repo.spring.io/snapshot</url><releases><enabled>false</enabled></releases></repository></repositories></project>
2.application.yaml
配置
spring:ai:mcp:server:name: webflux-mcp-serverversion: 1.0.0type: ASYNC # Recommended for reactive applicationssse-message-endpoint: /mcp/messages
定义MCP名称和版本号以及同步或异步配置。
3.定义工具类
@Service
public class DateTimeService {@Tool(description = "Get the current date and time in the user's timezone")String getCurrentDateTime() {return LocalDateTime.now().atZone(LocaleContextHolder.getTimeZone().toZoneId()).toString();}@Tool(description = "Set a user alarm for the given time, provided in ISO-8601 format")String setAlarm(String time) {LocalDateTime alarmTime = LocalDateTime.parse(time, DateTimeFormatter.ISO_DATE_TIME);return "Alarm set for " + alarmTime;}
}
定义二个工具:
1.获取当前日期和时间
2.设置提醒功能
4.暴露工具
@Configuration
public class McpWebFluxServiceExampleConfig {@Beanpublic ToolCallbackProvider dateTimeTools(DateTimeService dateTimeService) {return MethodToolCallbackProvider.builder().toolObjects(dateTimeService).build();}
}
5.启动MCP Server项目
启动项目发现注册的两个工具成功,可以端可以发现两个工具。到此MCP Server服务完成,SSE的端点路径:http://localhost:9090
,接下来是客户端连接使用服务端提供的工具。
6.MCP Client连接MCP Server
1.新建Spring Boot项目,然后引入starter
<dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-mcp-client</artifactId></dependency>
完整pom.xml
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.4.4</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.mcp.example</groupId><artifactId>mcp-client-example</artifactId><version>0.0.1-SNAPSHOT</version><name>mcp-client-example</name><description>mcp-client-example</description><dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>1.0.0-SNAPSHOT</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-openai-spring-boot-starter</artifactId><version>1.0.0-SNAPSHOT</version></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-web</artifactId></dependency><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-mcp-client</artifactId></dependency><dependency><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-test</artifactId></dependency></dependencies><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build><repositories><repository><name>Central Portal Snapshots</name><id>central-portal-snapshots</id><url>https://central.sonatype.com/repository/maven-snapshots/</url><releases><enabled>false</enabled></releases><snapshots><enabled>true</enabled></snapshots></repository><repository><id>spring-milestones</id><name>Spring Milestones</name><url>https://repo.spring.io/milestone</url><snapshots><enabled>false</enabled></snapshots></repository><repository><id>spring-snapshots</id><name>Spring Snapshots</name><url>https://repo.spring.io/snapshot</url><releases><enabled>false</enabled></releases></repository></repositories></project>
2.配置
spring:ai:openai:api-key: 你自己密钥base-url: https://api.siliconflow.cnchat:options:model: Qwen/Qwen2.5-72B-Instructmcp:client:sse:connections:server1:url: http://localhost:9090toolcallback:enabled: true
server:port: 9091
配置文件内容,大模型配置方便测试工具使用,mcp服务端设置就是mcp server提供的sse端点。
toolcalback.enable=true 自动注入Spring AI ToolCallbackProvider。
3.测试
package com.mcp.example.mcpclientexample;import io.modelcontextprotocol.client.McpAsyncClient;
import jakarta.annotation.Resource;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.mcp.SyncMcpToolCallbackProvider;
import org.springframework.ai.tool.ToolCallback;
import org.springframework.ai.tool.ToolCallbackProvider;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;import java.util.Arrays;
import java.util.List;@SpringBootApplication
public class McpClientExampleApplication implements CommandLineRunner {@Resourceprivate ToolCallbackProvider tools;@ResourceChatClient.Builder chatClientBuilder;public static void main(String[] args) {SpringApplication.run(McpClientExampleApplication.class, args);}@Overridepublic void run(String... args) throws Exception {var chatClient = chatClientBuilder.defaultTools(tools).build();String content = chatClient.prompt("10分钟后,设置一个闹铃。").call().content();System.out.println(content);String content1 = chatClient.prompt("明天星期几?").call().content();System.out.println(content1);}}
运行客户端项目:
结果表明定义的工具大模型根据用户的提问,选择了合适的工具进行回答。
STDIO Server
标准 MCP 服务器,通过 STDIO 服务器传输支持完整的 MCP 服务器功能。
<dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-mcp-server</artifactId>
</dependency>
1.创建Server项目
新建Spring Boot项目引入以下依赖
<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 https://maven.apache.org/xsd/maven-4.0.0.xsd"><modelVersion>4.0.0</modelVersion><parent><groupId>org.springframework.boot</groupId><artifactId>spring-boot-starter-parent</artifactId><version>3.4.4</version><relativePath/> <!-- lookup parent from repository --></parent><groupId>com.mcp.example</groupId><artifactId>mcp-stdio-server-example</artifactId><version>0.0.1-SNAPSHOT</version><name>mcp-stdio-server-example</name><description>mcp-stdio-server-example</description><dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-bom</artifactId><version>1.0.0-SNAPSHOT</version><type>pom</type><scope>import</scope></dependency></dependencies></dependencyManagement><dependencies><dependency><groupId>org.springframework.ai</groupId><artifactId>spring-ai-starter-mcp-server</artifactId></dependency></dependencies><build><plugins><plugin><groupId>org.springframework.boot</groupId><artifactId>spring-boot-maven-plugin</artifactId></plugin></plugins></build><repositories><repository><name>Central Portal Snapshots</name><id>central-portal-snapshots</id><url>https://central.sonatype.com/repository/maven-snapshots/</url><releases><enabled>false</enabled></releases><snapshots><enabled>true</enabled></snapshots></repository><repository><id>spring-milestones</id><name>Spring Milestones</name><url>https://repo.spring.io/milestone</url><snapshots><enabled>false</enabled></snapshots></repository><repository><id>spring-snapshots</id><name>Spring Snapshots</name><url>https://repo.spring.io/snapshot</url><releases><enabled>false</enabled></releases></repository></repositories></project>
配置文件application.yaml
spring:ai:mcp:server:name: stdio-mcp-serverversion: 1.0.0stdio: truemain:banner-mode: offweb-application-type: none
logging:pattern:console:
server:port: 9090
main:
banner-mode: off
web-application-type: none 这个配置非常关键,否则client与server通信会提示json解析有问题。这个必须关掉。
2.新建工具
与sse server一样,新建DateTimeTool并注册。
3.打包项目
STDIO方式server和client之间是进程间通信,所以需要把server打包成jar,以便client命令启动执行,或者三方客户端命令启动执行。将server jar放到一个指定目录,如下所示:
target/mcp-stdio-server-example.jar
4.创建client项目
直接使用上面sse server使用的 Clinet,修改对应配置文件application.yaml
和新建mcp-server配置json。
mcp-servers-config.json
。
{"mcpServers": {"stdio-mcp-server": {"command": "java","args": ["-Dspring.ai.mcp.server.stdio=true","-Dspring.main.web-application-type=none","-jar","mcp server正确的路径 ../mcp-stdio-server-example-0.0.1-SNAPSHOT.jar"],"env": {}}}
}
application.yaml
spring:ai:openai:api-key: sk-qwkegvacbfpsctyhfgakxlwfnklinwjunjyfmonnxddmcixrbase-url: https://api.siliconflow.cnchat:options:model: Qwen/Qwen2.5-72B-Instructmcp:client:
# sse:
# connections:
# server1:
# url: http://localhost:9090stdio:root-change-notification: falseservers-configuration: classpath:/mcp-servers-config.jsontoolcallback:enabled: true
server:port: 9091