Gremoire

What is MCP (Model Context Protocol)? ​

MCP, or Model Context Protocol, is a way for AI models (like ChatGPT) to share and use context in a structured and organized manner. Think of it as a “language” or “set of rules” that helps AI understand what it knows, what it should do, and how it should interact with tools or data.

In simple terms, MCP makes it easier for an AI to manage tasks, remember context, and interact with external systems in a predictable way.

How MCP Works (Simple Explanation) ​

Imagine you have a personal assistant AI. Without a protocol, it might get confused if you ask it to do multiple things at once:

  • Task 1: Write a file called notes.txt.
  • Task 2: Check the weather.

MCP helps the AI understand each task clearly, track what it has done, and handle multiple tools without mixing things up.

It works by:

  1. Defining the context – MCP tells the AI what it currently knows and what it can do.
  2. Providing instructions – MCP helps the AI decide which tool or function to use.
  3. Receiving results – MCP ensures the AI can store and process the outputs correctly.

Scenario-Based Explanation ​

Let’s say you have an AI that can do three things: write files, calculate numbers, and check the calendar.

Scenario: You ask the AI:

“Create a file called todo.txt with today’s tasks and then tell me what day it is.”

Without MCP, the AI might:

  • Start writing the file but forget to include all tasks.
  • Mix up the calendar info with the file content.

With MCP, the AI does this smoothly:

  1. Context: MCP tells the AI: “You can use a file-writing tool and a calendar tool.”
  2. Task 1: AI writes todo.txt using the file-writing tool. MCP tracks that this task is done.
  3. Task 2: AI checks the calendar tool. MCP ensures the result is returned clearly.

Result: You get a complete file and the correct day, without confusion.

Why MCP is Useful ​

  • Organized workflow: AI can handle multiple tasks without mixing them up.
  • Tool integration: AI knows which tools to use and how.
  • Predictable results: Tasks are executed clearly, step by step.

In short, MCP is like a traffic controller for AI tasks, making sure everything goes to the right place and nothing crashes into each other.