Build a Streamable HTTP MCP Server with Spring AI 1.1

This guide demonstrates how to develop a Streamable HTTP MCP Server based on Spring AI 1.1. It starts by pointing to the Spring AI GitHub repository (https://github.com/spring-projects/spring-ai/tree/main/spring-ai-spring-boot-starters) where four new Spring Boot starters for MCP Server are listed:

spring-ai-starter-mcp-server-streamable-webmvc

spring-ai-starter-mcp-server-streamable-webflux

spring-ai-starter-mcp-server-stateless-webmvc

spring-ai-starter-mcp-server-stateless-webflux

These starters enable Java developers to build MCP Servers with Streamable HTTP support.

Project Setup

Create a new Maven project and add the following dependencies to pom.xml (the essential parts are shown):

<dependency>
  <groupId>org.springframework.boot</groupId>
  <artifactId>spring-boot-autoconfigure-processor</artifactId>
</dependency>

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>org.springframework.ai</groupId>
      <artifactId>spring-ai-bom</artifactId>
      <version>1.1.0-SNAPSHOT</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-web</artifactId>
  </dependency>
  <dependency>
    <groupId>org.projectlombok</groupId>
    <artifactId>lombok</artifactId>
  </dependency>
  <dependency>
    <groupId>com.alibaba</groupId>
    <artifactId>fastjson</artifactId>
    <version>1.2.83</version>
  </dependency>
  <dependency>
    <groupId>org.jsoup</groupId>
    <artifactId>jsoup</artifactId>
    <version>1.18.3</version>
  </dependency>
  <dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-io</artifactId>
    <version>1.3.2</version>
  </dependency>
  <dependency>
    <groupId>org.apache.commons</groupId>
    <artifactId>commons-lang3</artifactId>
    <version>3.17.0</version>
  </dependency>
  <dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-server-streamable-webmvc</artifactId>
  </dependency>
</dependencies>

Configure the Spring snapshot repository to resolve the 1.1.0‑SNAPSHOT artifacts:

<repositories>
  <repository>
    <id>spring-snapshots</id>
    <name>Spring Snapshots</name>
    <url>https://repo.spring.io/snapshot</url>
    <releases><enabled>false</enabled></releases>
    <snapshots><enabled>true</enabled></snapshots>
  </repository>
</repositories>

Application Configuration

Add the following to src/main/resources/application.yml to enable Streamable HTTP support:

spring:
  ai:
    mcp:
      server:
        streamable-http:
          enabled: true
          name: springai-mcp-server
          version: 1.0.0
          type: ASYNC
        mcp-endpoint: /mcp
  server:
    port: 8001

The corresponding Java properties class is McpStreamableServerProperties.

Business Logic – Weibo Trending Service

The service fetches Weibo hot‑search data, filters ads, builds a list of maps, and returns a JSON string. The full source is:

@Service
@Slf4j
public class WeiboTrendingDataRequestService {
    @Tool(name = "get-weibo-trending",
          description = "获取微博热搜榜，包含时事热点、社会现象、娱乐新闻、明星动态及网络热议话题的实时热门中文资讯")
    public String getWeiboTrending() {
        String url = "https://weibo.com/ajax/side/hotSearch";
        try {
            // 1. 请求微博热搜接口（GET 请求）
            Connection.Response response = Jsoup.connect(url)
                .ignoreContentType(true)
                .timeout(10000)
                .header("referer", "https://weibo.com")
                .method(Connection.Method.GET)
                .execute();
            // 2. 判断 HTTP 状态码
            if (response.statusCode() != 200) {
                log.error("请求微博热搜失败，HTTP状态码: {}", String.valueOf(response.statusCode()));
                return "微博热搜获取失败，请稍后再试";
            }
            // 3. 解析返回的 JSON 数据
            String body = response.body();
            JSON.Object json = JSON.parseObject(body);
            if (json.getInteger("ok") != 1 || !json.containsKey("data")) {
                return "微博热搜获取失败，返回数据异常";
            }
            JSON.Array realtimeList = json.getJSONObject("data").getJSONArray("realtime");
            if (realtimeList == null || realtimeList.isEmpty()) {return "微博热搜为空";}
            // 4. 处理每一条热搜
            List<Map<String, Object>> resultList = new ArrayList<>();
            for (int i = 0; i < realtimeList.size(); i++) {
                JSON.Object item = realtimeList.getJSONObject(i);
                // 跳过广告
                if (item.getIntValue("is_ad") == 1) { continue; }
                String word = item.getString("word");
                String wordScheme = item.getString("word_scheme");
                String key = (wordScheme != null && !wordScheme.isEmpty()) ? wordScheme : "#" + word;
                // 构造链接
                String searchUrl = "https://s.weibo.com/weibo?q=" + URLEncoder.encode(key, StandardCharsets.UTF_8.toString()) + "&band_rank=1&Refer=top";
                // 封装成 map
                Map<String, Object> map = new HashMap<>();
                map.put("title", word);
                map.put("description", item.getString("note") != null ? item.getString("note") : key);
                map.put("popularity", item.getString("num"));
                map.put("link", searchUrl);
                resultList.add(map);
            }
            // 5. 转换为 JSON 字符串返回
            return JSON.toJSONString(resultList);
        } catch (Exception e) {
            log.error("工具调用异常: {}", e.getMessage(), e);
            return "工具调用失败，请稍后再试";
        }
    }
}

Main Application Class

Register the service as a tool callback and start the Spring Boot application:

@SpringBootApplication
public class ToolApplication {
    public static void main(String[] args) {
        SpringApplication.run(ToolApplication.class, args);
    }

    @Bean
    public ToolCallbackProvider applicationTools(WeiboTrendingDataRequestService weibo) {
        return MethodToolCallbackProvider.builder()
                .toolObjects(weibo)
                .build();
    }
}

Running and Testing

Start the application (default port 8001). In an MCP client, add a configuration pointing to http://localhost:8001/mcp. Send a test query; the server returns the processed Weibo hot‑search results, confirming successful integration.

Comparison of Spring AI Starters

The article notes that the two Streamable HTTP starters ( spring-ai-starter-mcp-server-streamable-webmvc and spring-ai-starter-mcp-server-streamable-webflux) internally use McpStreamableServerWebMvcAutoConfiguration (or the WebFlux counterpart) which delegates to WebMvcStreamableServerTransportProvider from the latest Java MCP Server SDK.

Conclusion

This tutorial shows how to leverage Spring AI 1.1 snapshot releases to build a Streamable HTTP MCP Server, covering repository discovery, Maven setup, configuration, service implementation, and verification steps.