To use MCP effectively, you need to understand how its parts connect. To understand MCP, think of it as a smart bridge between your code and an AI model. It lets you trigger code functions without writing or running scripts manually but using natural language. The system flows through receiving a command, sending it to the AI, running your function, and returning the result.

Let's say you have a small program that pulls data from an Excel file. Normally, you'd need to open a coding tool, type in the file location, sheet name, and what part you want, and then run the program yourself.

Let's say you want to check how much you spent on groceries last month. Instead of manually opening Excel, finding the file, picking the sheet, and adding it up, you can just ask the AI and get the total instantly.

The AI receives your request, understands the intent, and picks the right function. It auto-fills parameters like finding groceries.xlsx, selecting the right month, and reading the needed data. Using its LLM capabilities, it returns a clear, structured reply.

With MCP, you can create functions for local data, apps, or online services. For example, if you often analyze product performance in Excel, you can write functions for that — and connect them to the AI to run automatically when asked.