Building a Model Context Protocol (MCP) Server Using Java and Spring AI

Introduction

The Model Context Protocol (MCP) is becoming standard for creating scalable, context-driven applications that interact with AI models. This protocol provides a structured way to build servers that bridge the gap between AI models and client systems. In this blog post, we will explore MCP, why Spring AI is an excellent framework to implement it, and walk through the process of building, implementing, and testing an MCP server with Java.

What is MCP?

Model Context Protocol (MCP) is a lightweight, extensible protocol that facilitates communication between AI models and applications. It provides a framework to efficiently manage context, including queries, context lifecycle, and dynamic feedback, in a way that optimizes interaction with intelligent systems such as AI models or large language models (LLMs).

Key Features of MCP

1. Context Management: Allows applications to maintain and utilize session-based contexts for consistent interactions.
2. Scalability: Enables seamless scaling by maintaining state effectively.
3. Interoperability: Supports integration across multiple models and tools.
4. Extensibility: Designed for building custom workflows, enhancing flexibility.

Why Use Spring AI to Build an MCP Server?

Spring AI is a rapidly evolving library in the Spring ecosystem that provides support for integrating AI and ML models into modern applications. Using Spring AI to build an MCP server brings several advantages:

1. Ease of Development: Spring AI simplifies the integration of AI models with various interfaces, APIs, and protocols.
2. Microservice Architecture: The Spring framework is naturally built for microservices, making it ideal for scaling MCP implementations.
3. Integrated Tools: It offers features such as dependency injection, REST support, and configuration management that make MCP implementation easier and faster.
4. Community Support: Leveraging the robust Spring ecosystem ensures a wealth of resources for development and troubleshooting.

Steps to Build, Implement, and Test an MCP Server

1. Setting up Your Project

Start by creating a new Spring Boot project. Use Spring Initializr and include the following dependencies:

• Spring Web
• Spring AI
• Spring Boot DevTools
• Spring Boot Actuator (optional, for monitoring)
• Lombok (for reducing boilerplate code)

You can create the project either in an IDE like IntelliJ or via command-line tools.

Example Maven Dependencies:

<dependencies>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-web</artifactId>
    </dependency>
    <dependency>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-ai</artifactId>
    </dependency>
    <dependency>
        <groupId>org.projectlombok</groupId>
        <artifactId>lombok</artifactId>
    </dependency>
</dependencies>

2. Define the MCP Context Model

An important part of MCP is managing context efficiently.

Create a class that represents the ContextModel.

@Data
@AllArgsConstructor
@NoArgsConstructor
public class ContextModel {
    private String contextId;
    private String query;
    private String modelResponse;
    private long timestamp;
}

3. Create MCP Controller

This is the entry point for your server. It exposes endpoints for handling queries and managing contexts.

@RestController
@RequestMapping("/api/mcp")
public class MCPController {

    @PostMapping("/query")
    public ResponseEntity<String> handleQuery(@RequestBody ContextModel context) {
        // Simulating an AI interaction
        String response = "Response from AI Model for query: " + context.getQuery();
        context.setModelResponse(response);
        context.setTimestamp(System.currentTimeMillis());
        
        return ResponseEntity.ok(response);
    }

    @GetMapping("/context/{id}")
    public ResponseEntity<String> getContext(@PathVariable String id) {
        // Retrieve context by ID (mock data for demonstration)
        return ResponseEntity.ok("Context data for ID: " + id);
    }
}

4. Implement the Service Layer

Add a service layer that handles communication with the AI models.

@Service
public class MCPService {

    public String processQuery(String query) {
        // This is where you'd integrate with your AI model
        // Call the Spring AI libraries here for advanced capabilities
        return "Processed response for: " + query;
    }
}

5. Build and Run

To build the project, use Maven or Gradle:

mvn clean install

Run the application:

mvn spring-boot:run

Your MCP server is now running at http://localhost:8080/api/mcp.

6. Testing the MCP Server

Use tools such as Postman, cURL, or an integrated testing suite to validate your endpoints.

Example Test Query with cURL:

curl -X POST -H "Content-Type: application/json" \

-d '{"contextId":"1", "query":"What is MCP?"}' \

http://localhost:8080/api/mcp/query

Expected Response:

Response from AI Model for query: What is MCP?

Benefits of Using MCP with Spring AI

1. Seamless AI Integration: Spring AI provides abstractions over popular AI APIs, such as OpenAI, for efficient integration.
2. Improved Maintainability: The modular architecture of Spring Boot ensures that MCP servers remain clean and maintainable.
3. Scalability: By leveraging Spring Boot’s microservice model, MCP servers can be made scalable to handle large volumes of traffic.
4. Enhanced Performance: Context-based management optimizes query resolution and minimizes redundant interactions with AI models.
5. Developer Efficiency: With Spring Boot and Spring AI, you can focus on application logic rather than boilerplate code.

List of top MCP Servers

Here’s a categorized list of top Model Context Protocol (MCP) servers and their functionalities, making it easy to understand their roles and potential applications:

1. File and Code Management MCP Servers

• File System MCP Server: Provides direct interaction with the local file system. Enables LLMs (Large Language Models) to perform operations such as reading, writing, and creating directories.
  Use Case: Automating file-based workflows or managing local data programmatically.
• GitHub MCP Server: Allows integration with GitHub repositories. Capabilities include updating files, searching through code, and version control interactions.
  Use Case: Streamlining code collaboration or running AI-assisted code reviews.

2. Communication and Collaboration MCP Servers

• Slack MCP Server: Integrates with Slack’s API to enable interaction with workspaces, channels, and messages.
  Use Case: Automating Slack notifications, managing conversations, or creating chatbot workflows for teams.
• Notion MCP Server: Facilitates integration with Notion’s API to read, write, and manage documents, databases, or workspace items.
  Use Case: Enhancing productivity for managing notes and shared workspaces using AI.

3. Data and Database MCP Servers

• PostgreSQL MCP Server: Provides an LLM interface to inspect database schemas and execute read-only SQL queries.
  Use Case: Querying structured data efficiently or generating insights from large databases.
• Redis MCP Server: Offers access to Redis databases, allowing for key-value storage interactions.
  Use Case: Managing session states, caching, or querying fast in-memory data storage.

4. API Integration MCP Servers

• Google Maps MCP Server: Integrates with the Google Maps API to fetch location data, maps, and directions.
  Use Case: Building location-aware applications or AI-powered map navigation tools.
• Google Drive MCP Server: Enables reading, writing, and searching over files stored in Google Drive.
  Use Case: Searching file contents or automating cloud-based document workflows.
• Stripe MCP Server: Allows interaction with the Stripe API to manage payments, subscriptions, and accounts.
  Use Case: Enhancing automated billing and payment systems with AI.
• Docker MCP Server: Integrates with Docker APIs for managing containers, images, volumes, and networks.
  Use Case: AI-powered DevOps for streamlining containerized application deployment and management.

5. Search and External Data MCP Servers

• Brave MCP Server: Uses Brave’s Search API for web and local search, ensuring that the AI can access real-time knowledge.
  Use Case: Performing real-time, privacy-focused searches.
• Perplexity MCP Server: Connects to Perplexity’s Sonar API for real-time search, allowing quick access to external information.
  Use Case: Dynamic information retrieval for AI chat platforms.

This list highlights how MCP servers unlock the true potential of Large Language Models by giving them structured access to data, services, and real-world applications. By implementing and combining these servers, organizations can create highly intelligent, context-aware systems tailored for business or personal use cases.

Conclusion

By combining the Model Context Protocol (MCP) and Spring AI, developers can create responsive, reliable, and scalable servers for AI-driven applications. This tutorial walked you through the steps to set up an MCP server, demonstrating the simplicity and power of the Spring AI framework.

With this setup, you can now optimize communication with AI models, enhance your application’s capabilities, and scale efficiently. As AI adoption grows, having an MCP server ready positions your solution as adaptable and future-ready.

Summary

Article Name

Building a Model Context Protocol (MCP) Server Using Java and Spring AI

Description

By combining the Model Context Protocol (MCP) and Spring AI, developers can create responsive, reliable, and scalable servers for AI-driven applications. This tutorial walked you through the steps to set up an MCP server, demonstrating the simplicity and power of the Spring AI framework.

Author

Karthik

Publisher Name

Upnxtblog

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