How to Build a Personal Context Portfolio and MCP Server

In a world of agents, everything is about context. And today, we are going to help you build your own personal context portfolio and MCP server. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright, friends. Quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Blitsy, Robots and Pencils, and Superintelligent. To get an ad free version of the show, go to patreon.com/aidailybrief, or or you can subscribe at Apple Podcasts. If you are interested in sponsoring the show, send us a note at sponsors@aidailybrief.ai.aidailybrief.ai is also where you will find out everything that is going on in the AI DB ecosystem and where, and this one is going to be relevant for today's, you can access the companion experiences, which are not for every single show, but are for many of them. You can find that at play.aidailybrief.ai, and everything that I'm sharing in this episode will be linked there. Today, we have another episode in our Build Week series, and boy does this one cut to the heart of building right now. We officially live in the agentic era, and agents, as we know, need context to do their jobs well. And yet, context is one of those things that is very simple to articulate and much harder to actually organize in a way that is useful. Now this is obviously a big problem in the context of organizations. Michael Chen from Applied Compute recently dropped an article on X called what to expect when you're deploying AI in the enterprise. He writes, at Applied Compute, we spent the past six months embedding inside companies to deploy AI into production workflows, I e actually sitting in their offices, filing tickets, reading confluence pages, fighting for access to data, and shipping agents into production that improve over time. There is surprisingly little written about working with large organizations in the age of AI, so this is our attempt to fill that gap. And big and blaring right at the front is one, data ready is just a state of mind. The gap between we have data and we have data in a format that an AI system can learn from is enormous. It surprises everyone, even teams that have already wrangled internal data for incredible companies. Most enterprise data was never structured with AI consumption in mind. It's difficult to imagine a more challenging starting point for a data project, which at its core, every agent deployment is, a hard data problem. Now he's using the word data, but obviously, in this case, this is at least partially synonymous with context. One of the big differentiators between organizations that are leading and organizations that are lagging is that the lagging organizations tend to operate without their AI systems having access to context. In other words, they're dropping Copilot on people's heads and hoping it all works out, which is very different than …