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Java Enters the AI Arena with MCP and Quarkus

· 3 min read
Adrian Escutia
Adrian Escutia
A Rebel with a Cause, Innovating the Future

AI is reshaping the developer landscape, and Java refuses to be left behind. With the introduction of Model Context Protocol (MCP) into Java, thanks to the Quarkus framework, developers can now build AI-powered applications with minimal effort. As Max Andersen brilliantly put it in the Quarkus Insights video: "If AI doesn't come to Java, Java goes to AI."

AI is Booming, But Java Has Been Left Behind​

AI development is exploding across industries, with Python and TypeScript leading the charge. Python has long dominated AI and machine learning due to its rich ecosystem of libraries like TensorFlow, PyTorch, and scikit-learn. Meanwhile, TypeScript is making strides in AI-powered web applications and serverless environments.

However, Java—despite being a dominant force in enterprise applications—has largely been on the sidelines of the AI revolution. Many AI frameworks and models are optimized for Python, leaving Java developers with fewer native options for integrating AI into their stacks.

Now, that's changing.

What is MCP (Model Context Protocol)?​

MCP is an emerging protocol that enables applications to interact with AI models using structured context, making AI-driven solutions more predictable and manageable. It simplifies the way developers feed context to AI models and retrieve meaningful responses, leveraging SSE (Server-Sent Events) transport for real-time interaction.

Why is MCP in Java a Big Deal?​

With MCP landing in Java via Quarkus, developers can:

  • Easily integrate AI models into Java applications.
  • Use annotations to streamline AI interaction.
  • Expose AI-driven services with SSE transport for real-time responses.
  • Leverage Quarkus's fast startup times and small footprint, making it ideal for AI workloads in cloud and edge environments.

Hands-On: Getting Started with MCP in Java​

If you're ready to explore this exciting new integration, check out the full video demo here; by the day this article is published, I was not able to find the MCP extension in the Quarkus extension catalog, but you can dive into the Quarkus MCP Server GitHub repo here and the Quarkus MCP Servers GitHub repo here.

For those interested in expanding their AI capabilities even further, Agentico offers additional tooling for AI-powered agents in TypeScript. Learn more about it here.

What's Next?​

I'll be diving deeper into how MCP can be leveraged for real-world AI applications in Java, experimenting with Agentico and Quarkus to see how developers can supercharge their AI projects. Stay tuned for more insights and hands-on examples!

🚀 Java meets AI – let's build the future together! Go Rebels! ✊🏻

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