Tutorials on Ai Agents

Welcome to the second article in our Model Context Protocol (MCP) series! In the first article , we covered all the basics—what MCP is, how it works, and its key components. Not only that, we even built our first simple MCP server to put that knowledge into practice. If you’re new to this topic, I highly suggest you check out that first article before continuing here. It’ll give you a clear understanding of what’s going on and make this journey a lot smoother. Now, it’s time to step things up. In this article, we’re going to take what we’ve learned and build something more advanced – a custom MCP server from scratch. This is where things get interesting, because we’ll see just how flexible and powerful MCP can be with the new tooling we’re going to explore, and how we can shape MCP to fit our own needs. Let’s get into it! First, let’s quickly mention the tech stack that we’re going to work with. We’re going to use the same one from the previous article: TypeScript, Node.js and MacOS. If you're using a different tech stack, no worries, the key ideas will be the same. As an additional reference, you can also refer to documentation which includes basics for Python and Java too.

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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.

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