Java has seen a meteoric rise in AI through various AI agent libraries, such as LangChain4J and Embabel.
We've seen various platforms adopting MCP, such as Quarkus and Helidon, but these require running the MCP server on-premise.
But what if developers prefer the advantage of deploying an MCP service and exposing it as an HTTP service, just as they currently deploy their enterprise applications?

In this talk, I showcase an enterprise implementation of the MCP server that is fully powered by Jakarta EE features.
Developers will be introduced to MCP features (such as tools, prompts, resources and resource templates, logging, etc.), and how simple it is to integrate the MCP into their existing enterprise service application, with minimal configuration.
These features can be deployed to any Jakarta EE server of choice, using their existing deployment method of choice.

This talk will be beneficial for existing enterprise Java developers, as it will showcase the barrier to entry to learn such features to be minimal, and provide a little learning curve and have little impact on integration to existing business services, ensuring that developers can build AI applications with ease.