Last month, I had the fantastic opportunity to run a hands-on Model Context Protocol (MCP) workshop. It was a chance to mentor and coach colleagues, from engineers and testers to product owners, as we explored what MCP is, how it works, and why it is generating so much momentum across AI and developer communities.
My goal was not just to share knowledge. It was to guide the learning journey and build a shared, foundational understanding of this powerful emerging standard.
Workshop Highlights
We covered everything from core protocol basics and prompt crafting to real world patterns such as:
MCP standardises how AI applications communicate with external systems using JSON‑RPC 2.0 over stateful, bidirectional connections. Unlike standard JSON‑RPC, which treats every request independently, MCP augments the protocol by embedding session tokens and context IDs into each message.
This enhancement provides state, enabling advanced, multi‑step interactions and dynamic module loading at runtime. Such a stateful design is essential for modern, agile AI systems.
Imagine an AI‑powered payments system that dynamically integrates a new fraud detection module during operation. MCP allows the system to load this module on the fly without a full redeployment, provided the server supports dynamic module loading.
In this post, we'll explore the MCP protocol, its core components, and how it compares to traditional REST and GraphQL APIs.