MCP (Model Context Protocol) makes the buzz these days! MCP is a protocol invented last November by Anthropic, and integrated in Claude Desktop, and in more and more tools and frameworks, to expand LLMs capabilities by giving them access to various external tools and functions.

My colleague Philipp Schmid gave a great introduction to MCP recently, so if you want to learn more about MCP, this is the place for you.

In this article, I’d like to guide you through the implementation of an MCP server, and an MCP client, in Java. As I’m contributing to LangChain4j, I’ll be using LangChain4j’s mcp module for the client.

Right in the middle of a workshop I was delivering, as I was launching Google Cloud console’s Cloud Shell environment, I received the dreaded warning message: no space left on device.

And indeed, I didn’t have much space left, and Cloud Shell was reminding me it was high time I clean up the mess! Fortunately, the shell gives a nice hint, with a pointer to this documentation page with advice on how to reclaim space.

Since I started my career, I’ve been sharing what I’ve learned along the way in this blog. It makes me happy when developers find solutions to their problems, or discover new things, thanks to articles I’ve written here. So it’s important for me that readers are able to find those posts. Of course, my blog is indexed by search engines, and people usually find about it from Google or other engines, or they discover it via the links I share on social media. But with LLM powered tools (like Gemini, ChatGPT, Claude, etc.) you can make your content more easily grokkable by such tools.

With Large Language Models loving to output Markdown responses, I’ve been wanting to display those Markdown snippets nicely in the console, when developing some LLM-powered apps and experiments. At first, I thought I could use a Markdown parser library, and implement some kind of output formatter to display the text nicely, taking advantage of ANSI color codes and formats. However it felt a bit over-engineered, so I thought “hey, why not just use some simple regular expressions!” (and now you’ll tell me I have a second problem with regexes)

Retrieval Augmented Generation (RAG) is a great way to expand the knowledge of Large Language Models to let them know about your own data and documents. With RAG, LLMs can ground their answers on the information your provide, which reduces the chances of hallucinations.

Implementing RAG is fairly trivial with a framework like LangChain4j. However, the results may not be on-par with your quality expectations. Often, you’ll need to further tweak different aspects of the RAG pipeline, like the document preparation phase (in particular docs chunking), or the retrieval phase to find the best information in your vector database.

My colleague Jaana Dogan was pointing at the Anthropic’s MCP (Model Context Protocol) documentation pages which were describing how to build MCP servers and clients. The interesting twist was about preparing the documentation in order to have Claude assist you in building those MCP servers & clients, rather than clearly documenting how to do so.

MCP tutorials are great. There are no tutorials really.

"Copy these resources to Claude, and start asking some questions like..." pic.twitter.com/GG50DMWNLW

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In my previous article, I detailed how to build an AI-powered short story generation agent using Java, LangChain4j, Gemini, and Imagen 3, deployed on Cloud Run jobs.

This approach involved writing explicit Java code to orchestrate the entire workflow, defining each step programmatically. This follow-up article explores an alternative, declarative approach using Google Cloud Workflows.

I’ve written extensively on Workflows in the past, so for those AI agents that exhibit a very explicit plan and orchestration, I believe Workflows is also a great approach for such declarative AI agents.

This project demonstrates how to build a fully automated short story generator using Java, LangChain4j, Google Cloud’s Gemini and Imagen 3 models, and a serverless deployment on Cloud Run.

Every night at midnight UTC, a new story is created, complete with AI-generated illustrations, and published via Firebase Hosting. So if you want to read a new story every day, head over to:

→ short-ai-story.web.app ←

The code of this agent is available on Github. So don’t hesitate to check out the code:

First article of the year, so let me start by wishing you all, my dear readers, a very happy new year! And what is the subject of this new piece of content? For a while, I’ve been interested in analyzing trends and topics in social media streams. I recently joined Bluesky (you can follow me at @glaforge.dev), and contrarily to X, it’s possible to access its Firehose (the stream of all the messages sent by its users) pretty easily, and even for free. So let’s see what we can learn from the firehose!

Yesterday, Google released yet another cool Gemini model update, with Gemini 2.0 Flash thinking mode. Integrating natively and transparently some chain of thought techniques, the model is able to take some more thinking time, and automatically decomposes a complex task into smaller steps, and explores various paths in its thinking process. Thanks to this approach, Gemini 2.0 Flash is able to solve more complex problems than Gemini 1.5 Pro or the recent Gemini 2.0 Flash experiment.