How To Develop an MCP Server Using the Java SDK

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Modern AI interactions increasingly rely on context-aware models to provide coherent, accurate, and secure responses. To support this, the Model Context Protocol (MCP) was introduced—a lightweight HTTP-based protocol that manages memory, context, and interaction history for language models in a structured and interoperable way.

In this article, we’ll walk through how to develop an MCP server using the Java SDK, providing code examples, architectural guidance, and practical insights into implementing, extending, and deploying a fully functional MCP-compliant server.

What Is MCP?

The Model Context Protocol (MCP) standardizes the way AI models access, manipulate, and persist context over time. MCP separates the responsibilities of memory, context handling, and model logic, allowing for a modular AI system where servers handle:

  • Context storage (episodic memory, long-term memory)

  • Model state (chat threads, sessions)

  • Data injection (documents, vectors, instructions)

MCP uses standard HTTP methods (GET, POST, PUT, DELETE) and JSON-formatted payloads to perform CRUD operations on:

  • Sessions

  • Messages

  • Threads

  • Data blobs (files, documents)

Project Setup for an MCP Server in Java

Let’s begin by setting up the environment to build an MCP server using Java.

Dependencies

Create a new Maven project (mcp-server) and include the following in your pom.xml:

xml
<dependencies>
<!-- Spring Boot for REST APIs -->
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-starter-web</artifactId>
</dependency><!– Jackson for JSON serialization –>
<dependency>
<groupId>com.fasterxml.jackson.core</groupId>
<artifactId>jackson-databind</artifactId>
</dependency><!– Lombok for boilerplate reduction –>
<dependency>
<groupId>org.projectlombok</groupId>
<artifactId>lombok</artifactId>
<optional>true</optional>
</dependency><!– Spring Boot DevTools –>
<dependency>
<groupId>org.springframework.boot</groupId>
<artifactId>spring-boot-devtools</artifactId>
<scope>runtime</scope>
</dependency>
</dependencies>

Define the Core Domain Model

Let’s start with the essential entities of the MCP server: Session, Message, and Thread.

Session.java

java
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Session {
private String id;
private String modelId;
private Instant createdAt;
private Map<String, Object> metadata = new HashMap<>();
}

Message.java

java
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Message {
private String id;
private String sessionId;
private String role; // "user" or "assistant"
private String content;
private Instant timestamp;
}

Thread.java

java
@Data
@AllArgsConstructor
@NoArgsConstructor
public class Thread {
private String id;
private List<Message> messages = new ArrayList<>();
}

Implement In-Memory Storage (for Demo Purposes)

To keep things simple, we’ll use ConcurrentHashMap for temporary storage.

java
@Component
public class InMemoryStorage {
private final Map<String, Session> sessions = new ConcurrentHashMap<>();
private final Map<String, Thread> threads = new ConcurrentHashMap<>();public Session saveSession(Session session) {
sessions.put(session.getId(), session);
return session;
}public Session getSession(String id) {
return sessions.get(id);
}public void deleteSession(String id) {
sessions.remove(id);
}public Thread saveThread(Thread thread) {
threads.put(thread.getId(), thread);
return thread;
}public Thread getThread(String id) {
return threads.get(id);
}public void deleteThread(String id) {
threads.remove(id);
}
}

Expose MCP-Compliant Endpoints

Let’s expose HTTP endpoints that comply with the MCP design pattern using @RestController.

SessionController.java

java
@RestController
@RequestMapping("/v1/sessions")
@RequiredArgsConstructor
public class SessionController {private final InMemoryStorage storage;@PostMapping
public ResponseEntity<Session> createSession(@RequestBody Session request) {
request.setId(UUID.randomUUID().toString());
request.setCreatedAt(Instant.now());
return ResponseEntity.ok(storage.saveSession(request));
}@GetMapping(“/{id}”)
public ResponseEntity<Session> getSession(@PathVariable String id) {
Session session = storage.getSession(id);
return session != null ? ResponseEntity.ok(session) : ResponseEntity.notFound().build();
}@DeleteMapping(“/{id}”)
public ResponseEntity<Void> deleteSession(@PathVariable String id) {
storage.deleteSession(id);
return ResponseEntity.noContent().build();
}
}

ThreadController.java

java
@RestController
@RequestMapping("/v1/threads")
@RequiredArgsConstructor
public class ThreadController {private final InMemoryStorage storage;@PostMapping
public ResponseEntity<Thread> createThread(@RequestBody Thread thread) {
thread.setId(UUID.randomUUID().toString());
return ResponseEntity.ok(storage.saveThread(thread));
}@GetMapping(“/{id}”)
public ResponseEntity<Thread> getThread(@PathVariable String id) {
Thread thread = storage.getThread(id);
return thread != null ? ResponseEntity.ok(thread) : ResponseEntity.notFound().build();
}@PostMapping(“/{id}/messages”)
public ResponseEntity<Thread> addMessage(@PathVariable String id, @RequestBody Message msg) {
Thread thread = storage.getThread(id);
if (thread == null) return ResponseEntity.notFound().build();msg.setId(UUID.randomUUID().toString());
msg.setTimestamp(Instant.now());
thread.getMessages().add(msg);return ResponseEntity.ok(storage.saveThread(thread));
}
}

Sample Usage With curl

Create a Session

bash
curl -X POST http://localhost:8080/v1/sessions \
-H "Content-Type: application/json" \
-d '{"modelId": "gpt-4", "metadata": {"userId": "123"}}'

Create a Thread

bash
curl -X POST http://localhost:8080/v1/threads \
-H "Content-Type: application/json" \
-d '{}'

Add a Message to a Thread

bash
curl -X POST http://localhost:8080/v1/threads/{threadId}/messages \
-H "Content-Type: application/json" \
-d '{"role": "user", "content": "What is MCP?"}'

Security and Authorization

You may add basic authentication, API key headers, or OAuth2 if you want to secure access:

java
@Configuration
public class SecurityConfig extends WebSecurityConfigurerAdapter {
@Override
protected void configure(HttpSecurity http) throws Exception {
http.csrf().disable().authorizeRequests().anyRequest().permitAll(); // Replace for production
}
}

Extending the MCP Server

Here are advanced ideas to bring your server closer to a real-world implementation:

  • Persistence: Use PostgreSQL or MongoDB instead of in-memory maps.

  • Vector Database: Store embeddings and context-aware search using Pinecone, Weaviate, or Qdrant.

  • Plugin System: Add endpoints for tools or plugins like calendar, retrieval, or calculator.

  • LLM Middleware: Connect this MCP server to an OpenAI, Claude, or local LLM backend.

  • File Handling: Store and retrieve blobs or documents using /v1/files.

Benefits of MCP Compliance

  1. Interoperability: MCP-compliant systems can plug into OpenAI or other ecosystem tools without vendor lock-in.

  2. Modularity: You can decouple storage, memory, and model logic.

  3. Statefulness: Provides chat history, file context, and session memory natively.

  4. Fine-tuning UX: Control how LLMs interact based on context objects like memory, documents, or metadata.

Deployment Considerations

For production:

  • Use HTTPS

  • Add logging with Logback or SLF4J

  • Deploy with Docker:

Dockerfile
FROM openjdk:17
COPY target/mcp-server.jar app.jar
ENTRYPOINT ["java", "-jar", "/app.jar"]

  • Deploy to:

    • Heroku

    • AWS ECS/Fargate

    • Google Cloud Run

    • Azure App Service

Conclusion

Building an MCP-compliant server in Java offers you deep control over how your applications interact with large language models—allowing for context-rich, memory-aware, and extensible AI interactions. By leveraging Spring Boot and REST principles, you can construct a modular service that supports sessions, threads, messages, files, and plugins—all through simple HTTP interfaces.

This architecture allows you to scale independently (e.g., swap memory storage from in-memory to Redis or Postgres), and interoperate with existing tools, whether you’re working with OpenAI Assistants, LangChain agents, or home-grown LLM solutions.

By implementing your own MCP server:

  • You control the data (privacy, GDPR compliance),

  • You tailor the context logic to your use case (e.g., memory expiry, prioritization),

  • And you prepare for agentic behavior, where LLMs maintain context over long-term projects, tasks, or documents.

This is just the beginning. MCP is shaping up to become a core protocol for AI-native applications, and being able to build and customize your own MCP server opens the door to a future where context isn’t just retained—it’s deeply understood and leveraged to its full potential.