Over the past few days, I’ve been deep diving into something that’s becoming a real game-changer in how AIs interact with systems: the Model Context Protocol (MCP).

I had heard about it in passing, but it really clicked when I started playing with tools like PulseMCP, mcp.so, and especially when I integrated everything into Cline (VSCode) and Claude Desktop.

But more than just understanding it — I implemented it. I built a POC where, through MCP, the AI could:

• Access APIs like Notion and Gitbook
• Execute read/write operations with sources like Postgres
• Trigger backend actions as if navigating app workflows

All of this through tools exposed via MCP. And the experience was magical.

🧠 So, what exactly is MCP?

The Model Context Protocol is a specification that defines how a language model can discover, understand, and use external functionalities (tools).

Unlike REST APIs or even GraphQL, MCP focuses on describing capabilities, not just data. The AI understands "what it can do" through a structure called reflection.

🔍 Reflection: the secret to autonomy

Each MCP server exposes a /reflection endpoint listing all available tools — with their names, descriptions, and expected parameters.

The model reads this structure on its own, interprets it, and decides when and how to use each tool — no pre-prompts or manual injection needed. It’s like the model opens the documentation by itself.

In practice:

• You no longer need to describe tools inside the prompt
• No massive JSON payloads in each call
• The AI navigates the exposed catalog autonomously

This reduces tokens, improves performance, and makes the system far more modular and scalable.