What is MCP? The Model Context Protocol explained for non-engineers

This guide is for anyone who uses AI tools at work and wants to understand what MCP is, what it changes day to day, and what to check before using it. No technical background required.

What problem does MCP solve?

Picture a house where every device needs its own charger. One for the phone, another for the laptop, a third for the headphones. That's roughly where AI tools were until recently: each tool needed a custom connection to each app.

If a company wanted ChatGPT to read data from its app, it built a ChatGPT-specific integration. Then another for Claude, and another for the next tool. That's repetitive and expensive, so most AI tools stayed cut off from the rest of your software. You paid the price by copying information out of one app, pasting it into a chat, and carrying the answer back by hand.

The official MCP site compares the standard to a USB-C port for AI applications: one port with a known spec that any device built to that spec can plug into.

How one standard changes things

With MCP, an app maker builds one connection that follows the standard, and any AI tool that supports MCP can use it. As a user, you add the app's address in your tool's settings and sign in, and the tool can reach the app.

How does MCP work, without the jargon?

Every connection has two sides:

• The AI tool: Claude, ChatGPT and so on. It writes your answers.
• The service: the app you want the tool to reach, such as your calendar, your files or your memory. In MCP terms this is an "MCP server".

The service tells the tool what it can do, for example "search notes" or "save a note". When you ask for something that needs the service, the tool uses one of those actions, then writes your answer.

A workday example

You ask Claude: "What did I agree with Northwind about the reports?" Claude sees that the question needs your memory, so it asks the memory service to search for "Northwind" and "reports". The service returns the matching note, and Claude writes the answer from it.

You saw none of that. You asked a question, and the answer came back grounded in your notes.

What kinds of services connect this way?

People use MCP to connect their tools to many kinds of services. Some of the most common:

• Files and documents, so the tool can read a file or search a folder.
• Task and project tools, so the tool can create a task or summarise a project's status.
• Memory services, so the tool saves what you agree on and finds it in later chats.

A memory service is a special case because it connects your tools to each other, not just to one app. Every tool writes to the same place and reads from it.

How does Memory Bridge use MCP?

Memory Bridge is a memory service built on MCP. You add its address in your tool's settings and sign in with a code sent to your email, and the tool gets actions to search, read, write and organise your notes.

Because the standard is shared, Claude, ChatGPT, Codex, Cursor, VS Code and Gemini can all connect to the same memory, each from its own settings. What Claude saves, ChatGPT can find, and what you save in Cursor shows up in Claude.

The memory itself is plain-text Markdown notes, each with its source and date. You read and edit them from your account and can stop any tool's access whenever you like. A connected tool handles what it reads under its provider's own policy. If you're technical, our developers page covers the details.

Your next step

If the idea makes sense, try it. The simplest start is to connect one tool to a memory service, save one fact, and ask about it in a fresh chat.

Try one memory across your AI tools