Don't worry. Not everyone knows it...

I’m excited to share my most awaited video, where I break down how to build an MCP server completely from scratch using FastAPI —with no SDKs, no decorators, and absolutely no hidden magic.  This tutorial walks through the real MCP protocol step‑by‑step. You’ll see how the manifest, tools list, execution flow, and even streaming responses come together using pure Python. It’s a simple, transparent way to understand MCP at its core. 🎥 Watch the full video: 

Let’s be honest: most AI demos feel like magic tricks.  You type something, it replies. Cool!  But what happens when you want your AI to actually do something?  Like read a file, s ummarize a document, c all an API, r un a script, t rigger a workflow, etc. That’s where the GitHub Copilot SDK and MCP servers come in. They let you build real AI agents — ones that can reason, call tools, and interact with your environment like a tiny software teammate. In my latest video, I show you how to wire up local and remote MCP servers into a GitHub Copilot SDK agent. You’ll learn: - How MCP works  - How to build your own MCP server in Python - How to plug it into your agent - How to mix local and remote tools like a pro 👉 Watch the full walkthrough here If you’ve ever wanted to build an AI agent that feels like a real part of your stack — not just a chatbot — this is the video to watch. Let me know what you build after watching. I’m curious.

In this full tutorial, I’ll walk you through the complete workflow of creating an MCP (Model Context Protocol) server, deploying it on Azure App Service, and integrating it with AI agents using the AI Toolkit in Visual Studio Code. Whether you're a developer, AI enthusiast, or cloud architect, this video covers everything you need to: ✅ Build your MCP server ✅ Deploy it to Azure App Service  ✅ Secure and scale your server for production ✅ Connect it to AI agents for real-time tool invocation ✅ Troubleshoot, optimize, and monitor your deployment   If you're more interested in reading, then here is my detailed blog on MEDIUM

AI models are powerful, but their utility is often limited by their inability to interact with external systems efficiently. The Model Context Protocol (MCP) is designed to bridge this gap, allowing AI models to integrate seamlessly with external tools, APIs, and real-time data sources. By standardizing this interaction, MCP enables AI assistants to provide more informed, precise, and interactive responses.                                                            image generated from copilot What is MCP? MCP is an open protocol designed to improve how applications communicate context to Large Language Models (LLMs) . It allows AI models to access relevant information from external sources dynamically, reducing reliance on static training data and enhancing responsiveness. MCP supports multiple interaction method...