Shaaf's blog

There is always a distinct thrill in learning something new and immediately putting it to the test. My journey with Model Context Protocol (MCP) servers began with a basic ‘books API’ demo, but I quickly wanted to take it a step further and build something with real-world utility. Since I enjoy working with Keycloak, I thought: Why not create an MCP server for it?

The vision was simple: enable developers to just ‘chat’ with Keycloak. There are so many standard tasks—setting up new users, groups, clients, and browser workflows—that could be streamlined through conversation. For those unfamiliar, Keycloak is an open-source identity and access management solution. I released the first experimental version this past summer, and since then, the wave of constructive community feedback has been incredible. That momentum is exactly what gets me excited to keep building.

Recently I wrote an article about “Adoption of the Model Context Protocol Within the Java Ecosystem”. Now it was also time to start experimenting with writing an MCP Server myself (well maybe not the first time). Certainly I don’t want to be left out of all the cool things being demonstrated by the community. The goal for me is to learn, and creating perhaps a more practical example. In this post I am going to choose Keycloak, and write an experimental MCP server implementation for keycloak. The post is also to spark interest around this topic. Will it be useful to have an MCP server for Keycloak?

Langchain4j is my favorite framework for working with large language models and Java. In the last couple of weeks, both Bazlur and I have presented to multiple user groups and conferences. This week, we had the privilege of presenting at the Jakarta Tech Talk, which both of us were looking very much forward to.

We now have so much demo code on the topic that we cannot present all the variations in one hour. We are still building along as we learn here.

Konveyor AI is a tool used to migrate Java applications to different Java frameworks, such as from JavaEE to Quarkus or Spring or from Spring 5 to 6, using Generative AI and static code analysis. I wrote a detailed post about this last year for the Java Advent Calendar.

Most recently, we have all been hard at work, bringing a preview for our community of users. In this post, I will outline how you can install and configure Konveyor AI using OpenAI and make meaningful generations. However, I have chosen OpenAI for the sake of simplicity in this post. Users can choose many other models, which are documented here.