Java and MCP: How to Build Servers and Clients for Model Context Protocol

Introduction to Model Context Protocol and the Opportunity in the Java Ecosystem

Artificial intelligence development took a significant leap when models started interacting not just with typed text, but with real-world tools and data. In practice, this means instead of merely guessing the next word, an AI can query a database, read a log file, or trigger an external API. To enable this communication in an organized manner, the Model Context Protocol, known as MCP, was created. It is an open standard designed to standardize how AI assistants connect to local and corporate data sources. For the Java community, which historically prioritizes robustness, strict typing, and solid enterprise ecosystems, MCP represents the ideal bridge to integrate intelligent agents into legacy systems and modern microservices cleanly.

Architecture and Core Concepts of the MCP Protocol

Understanding MCP architecture requires looking at the classic division between client and server, adapted for the language model universe. The MCP client usually resides in the artificial intelligence application or conversational interface that the user manipulates daily. Meanwhile, the MCP server is a lightweight program, written in languages like Java, Python, or TypeScript, that translates AI requests into concrete actions within the operating system, relational databases, or internal APIs. Communication between them occurs through standardized channels, such as standard terminal input and output known as Stdio, or via Server-Sent Events, which are continuous data transmissions over the web. In practice, this separation ensures that the AI model remains decoupled from business logic and company infrastructure, increasing security and easing code maintenance.

Within the protocol ecosystem, servers expose three main primitives that structure the entire interaction: resources, tools, and prompts. Resources act as passive files or data that the AI can read to gain context, such as the contents of a technical specification document. Tools, in turn, are executable actions that the AI can trigger, like a Java function that checks an account balance or restarts a container in a staging environment. Finally, prompts are pre-formatted instruction templates that guide the AI on how to interact with the server in an optimized way. For the Java developer, mapping these primitives means structuring clean classes and interfaces, leveraging features like annotations and dependency injection to keep code organized and testable.

Preparing the Environment and Configuring the Java Project

The first practical step to build an MCP solution in Java is configuring the dependency manager, whether Maven or Gradle, by adding essential libraries for data transport and protocol manipulation. Since MCP uses the JSON-RPC format for structured message exchange, we need efficient libraries for object serialization and deserialization, such as Jackson. In practice, we create a modular project where the transport layer manages the incoming and outgoing byte stream, while the business layer processes received commands. This separation is crucial to ensure communication failures do not corrupt the main application state, keeping the system resilient even under heavy simultaneous AI request loads.

Initial project setup must include domain classes representing protocol messages, mapping requests, responses, and error notifications. In modern Java, using records is highly recommended for these immutable data structures, as they drastically reduce boilerplate code and ensure greater safety when handling payloads. Furthermore, configuring a robust logging system with SLF4J and Logback from the start is essential, since debugging asynchronous communication between an AI model and a local server requires total visibility over every JSON packet exchanged between endpoints.

Implementing a Functional MCP Server in Java

Creating an MCP server in Java involves implementing the connection lifecycle, listening to requests sent by the client, and responding according to protocol specifications. In practice, the server starts by waiting for commands in the terminal or via network connection, processes the received JSON message, identifies which tool or resource was requested, and executes the corresponding business logic. Below is a simplified example of a Java class that processes an available tools listing, using modern language features to keep the code clean and readable.

import java.util.List;
import java.util.Map;

public class McpServerHandler {
    public Map handleListTools() {
        return Map.of(
            "tools", List.of(
                Map.of(
                    "name", "check_system_status",
                    "description", "Verifies the health of core services",
                    "inputSchema", Map.of(
                        "type", "object",
                        "properties", Map.of()
                    )
                )
            )
        );
    }
}

Beyond listing tools, the server must implement the call dispatcher, which intercepts execution requested by the AI and returns the properly formatted result. If the invoked tool is a database query using frameworks like JDBC or Spring Data, the server executes the query securely, handles potential timeouts or connection failures, and packages the result into a textual format understandable by the language model. This conversion is crucial because the AI does not understand complex business objects directly, requiring the Java developer to act as a translator between the relational or object-oriented world and the structured text stream.

Developing an MCP Client in Java for Data Consumption

Just as we can create servers, the Java ecosystem allows building MCP clients capable of connecting to external servers to extract data or execute tools. In practice, a Java client acts as a conductor that initializes the partner server process, establishes the bidirectional communication channel, and sends resource discovery requests. This is extremely useful in corporate scenarios where legacy systems need integration with proprietary or open-source AI tools executed locally on company infrastructure.

To manage this communication asynchronously and prevent application lockups, using reactive programming or dedicated threads is highly recommended. The client sends JSON-RPC requests, awaits the asynchronous response, and validates the integrity of returned data before passing it to the rest of the business flow. Proper implementation of timeout handling and automatic reconnections ensures that if the MCP server crashes or restarts, the Java client application can re-establish the channel without manual intervention, maintaining the operational continuity of intelligent agents.

Security Considerations, Challenges, and Best Practices

Connecting language models directly to internal systems through MCP introduces inherent risks that require mitigation with technical rigor. In practice, allowing an AI to execute commands on databases or operating systems opens doors to vulnerabilities if strict validations are not applied. The principle of least privilege must guide Java server design: the service account or execution context under which the server runs must have strictly necessary access to perform its function, blocking any attempt to read or write to sensitive directories and tables.

Another critical point is sanitizing input parameters received from tools. Since text generated by language models can contain malicious instructions injected by bad actors, the Java service layer must never execute concatenated dynamic queries or system commands without proper parameterization. The use of prepared statements, rigorous type validation, and clear limits on returned response sizes ensures the application remains stable, secure, and immune to data exfiltration or arbitrary code execution attacks.

Conclusion

The adoption of the Model Context Protocol in the Java ecosystem marks a major milestone in consolidating artificial intelligence agent-driven architectures. By standardizing how conversational models access databases, files, and APIs, the protocol eliminates the need for custom, fragile integrations, bringing traditional software engineering robustness into the LLM universe. Developers and architects who master creating MCP clients and servers in Java gain the ability to build highly integrated, secure enterprise solutions ready for the future of cognitive computing.

Successful implementation of these architectures depends directly on attention to design details, such as responsibility decoupling, resilient error handling, and rigorous security in handling external data. With a solid Java foundation, modern tools, and a clear understanding of operational trade-offs, engineering teams can transform generic AI assistants into powerful productivity tools connected directly to the organization's vital systems.