Recently, I’ve been exploring the Model Context Protocol (MCP) and how to easily create custom servers to extend the capabilities of AI assistants like Gemini CLI which I use daily.

I wanted a way to build these servers in Java without the heavy boilerplate of a traditional Maven or Gradle project, or with a complex framework. The solution? Combining JBang, LangChain4j, and… 🥁… a custom Gemini CLI skill!

In this post, I’ll walk you through how I streamlined the creation of MCP STDIO servers, by creating an agent SKILL.md to replicate what I had learned in my previous article.

By now, you’re certainly all familiar with the Model Context Protocol (MCP)? It’s the standard for connecting Large Language Models (LLMs) to tools and data. But if you look at the current ecosystem, you’ll see a lot of Python and TypeScript…

As a Java developer, you might be wondering: How can I easily and quickly run my own MCP servers?

On this blog, I’ve explained how to develop MCP servers with Quarkus and Micronaut. But thanks to a recent community contribution to LangChain4j, and the simplicity of JBang, building a local MCP server in Java is even easier and with zero boilerplate.