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Udaya Veeramreddygari
Udaya Veeramreddygari

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Spring Boot + MCP: A Beginners Guide

#springboot #mcp #cloudnative #microservices

In today’s AI-driven cloud landscape, the lack of standardized Model Context Protocols (MCPs) often leads to deployments that are fragmented, unclear, and tough to manage. AI models are commonly trained, fine-tuned, and launched across different platforms with little insight into the assumptions, environmental factors, or usage limits that shaped their creation. This absence of contextual grounding makes it challenging for cloud providers, businesses, and regulators to trace model lineage, evaluate proper usage, or avert negative outcomes. For instance, a model designed for a specific demographic or legal area can be used elsewhere without any safeguards, which could result in bias, legal issues, or performance problems, all without anyone being held accountable.

By integrating MCPs into cloud-based AI systems, organizations can seamlessly weave essential metadata and contextual insights into every stage of a model's lifecycle from training and deployment to monitoring and eventual decommissioning. This integration empowers cloud platforms to offer model hosting that is aware of policies, along with automated safeguards and services that adapt to context (like location-based restrictions or compliance adjustments). Developers, users, and regulators all gain from clear documentation detailing the model's purpose, appropriate usage, and known risks fostering a more secure, transparent, and ethical AI landscape. In this way, MCPs turn cloud AI from a mysterious black box into a well-managed infrastructure that promotes dynamic policy alignment and builds societal trust on a large scale.

Let's dive into how can we develop simple MCP server using Spring boot.

Prerequisites

  • Java Development Kit (JDK). Version 17 preferred.
  • Maven
  • Spring Boot 3.3.6
  • Spring AI BOM 1.0.0-SNAPSHOT

Setting Up Your Project
Create a new spring boot project. The easiest way is to use the Spring Initializer at start.spring.io.

The project/folder structure to be followed.

├── pom.xml
├── README.md
└── src/
    └── main/
        ├── java/
        │   └── org/springframework/ai/mcp/server/
        │       ├── McpServerApplication.java
        │       └── service/
        │           ├── CourierService.java
        │           └── WeatherService.java
        └── resources/
            └── application.properties
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Dependencies
The project uses:

<parent>
    <groupId>org.springframework.boot</groupId>
    <artifactId>spring-boot-starter-parent</artifactId>
    <version>3.3.6</version>
</parent>

<dependencies>
    <dependency>
        <groupId>org.springframework.ai</groupId>
        <artifactId>spring-ai-starter-mcp-server-webmvc</artifactId>
    </dependency>
</dependencies>
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MCP Boot Starter Clients
The project uses the Spring AI Boot Starter for MCP Server:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-server-webmvc</artifactId>
</dependency>
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This starter provides:

  • Automatic configuration of MCP server endpoints
  • Integration with Spring Boot's auto-configuration
  • Support for both SSE and STDIO transport modes
  • Built-in tool callback registration
  • Integration with Spring's dependency injection

To create a client application, use the corresponding client starter:

<dependency>
    <groupId>org.springframework.ai</groupId>
    <artifactId>spring-ai-starter-mcp-client</artifactId>
</dependency>
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This project has WeatherService and CourierService implemented as a skeleton for further extensions.

Sample code on how to generate a new service/tool

@Service
public class NewService {
    @Tool(description = "Description of what the tool does")
    public String newTool(String param) {
        // Implementation
    }
}

// In McpServerApplication.java
@Bean
public ToolCallbackProvider newTools(NewService newService) {
    return MethodToolCallbackProvider.builder()
        .toolObjects(newService)
        .build();
}
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Building the Project

  • Make sure you have Maven and JDK installed: 
mvn --version
java --version
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  • Build the application using Maven: 
mvn clean install
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The build will create a JAR file at target/java-mcp-tools-0.0.1-SNAPSHOT.jar

Running the Application

  • Using Maven (recommended for development): 
mvn spring-boot:run
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  • Or using the generated JAR: 
java -jar .\target\java-mcp-tools-0.0.1-SNAPSHOT.jar
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Final Words
Model Context Protocols (MCPs) play a crucial role in the modern AI and cloud environment. They embed vital details about how a model should be used, its limitations, and the context in which it operates right into its lifecycle. Without MCPs, AI models can end up functioning like black boxes, disconnected from their surroundings, which raises the chances of misuse, bias, and failing to meet regulations. By incorporating MCPs, organizations can achieve greater transparency, enhance governance, and make sure that AI systems act responsibly and ethically across various applications and regions. This approach not only fosters trust among users and regulators but also sets the stage for scalable, safe, and aligned AI innovation in the cloud era.

HAPPY LEARNING and HAPPY CODING
You can find full code here. Feel free to contribute by following the guidelines.

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