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If you’re into AI agent development, you’ve probably started hearing more and more about a new emerging protocol – Model Context Protocol ( MCP ). In essence, this protocol simplifies how AI agents connect to the data and tools they need. By standardizing these connections, MCP reduces the extra work developers usually have to deal with. Essentially, it replaces the need to directly manage multiple APIs in your AI agent with one unified protocol. And lets you to add/remove any external tools for your agent with incredible ease. Making it more convenient to build complex and flexible AI systems. In this article, we’ll walk you through everything you need to know about MCP—from its core components and main concepts to practical implementations. We will focus specifically on building an MCP server, as it is likely the most useful and frequently used part of the MCP architecture that you will want to implement. So, let’s go! The Model Context Protocol, or MCP, is a simple standard, designed and open sourced by Anthropic to help AI tools talk to the systems where data lives. Think of it like a USB-C port, but for AI applications. Just as a USB-C port lets you connect different devices with one common plug, MCP lets AI models easily connect to various data sources and tools without needing custom code for every connection.

As AI continues to evolve, we’re constantly finding new ways how to improve and to use it. Today, AI has gone much further being just a chat tool. And one of these significant evolutionary steps is the creation and adoption of AI agents. With agents, you can deploy AI solutions that autonomously perform real-world tasks, for example: managing customer support, processing large amounts of information in real-time, and much more! Basically, any task that benefits from working with real-time data and reasoning capabilities. This series of articles will help you not only to grasp the fundamentals of AI agents, but also to get a practical experience of building one yourself. Covering crucial theoretical knowledge and concepts, as well as also learning how to properly apply them in the real world.

Welcome! This is part 4 of our course on how to build fullstack apps with Bolt and Supabase If you’re just joining, I highly recommend you take the course in the correct order before diving into this one. Here you can find Part 1 , Part 2 , Part 3 .

Hello and welcome! This is the 3rd lesson in our series about how to build complete fullstack applications in less than an afernoon with Bolt and Supabase. In the first 2 lessons, we talked about what exactly is Bolt in the first place, and what’s Supabase. If you want to read those first, here is Part 1 and Part 2 .

What if I told you that 2-3 hours from now you could have taken your app idea and transformed it into a beautiful, production-level full stack application, deployed and available on the internet, for everyone to use? If I told you something like this a couple of years ago, you’d laugh and scoff and dismiss everything I just said. In fact, this was my reaction too when I first heard someone from Supabase talk about what Bolt and Supabase combined could achieve.