What is MCP? The protocol that gives your AI a memory

You’ve probably lived this: you explain to ChatGPT in detail what you’re working on — and next session, it’s gone. You switch to Claude because it fits the task better — and start from zero again. The models get smarter with every release, but one thing they can’t do out of the box: remember anything that lives outside the current conversation.

That’s the problem behind an unassuming acronym you now see everywhere: MCP.

The short answer

MCP stands for Model Context Protocol — an open standard that defines how AI applications talk to external data sources and tools. Anthropic published it in late 2024 and deliberately built it as an open protocol rather than an exclusive feature. By now virtually every relevant AI client supports it: Claude, ChatGPT, Cursor, and many more.

The common analogy holds up: MCP is the USB-C port for AI. Before USB-C, every device needed its own cable. Before MCP, every connection between an AI and a data source needed its own hand-built integration. With MCP there is one plug that fits everywhere: a service offers its capabilities once as an “MCP server” — and every AI client that speaks the protocol can use them.

How it works in practice

Behind the protocol sits a simple division of roles:

• The client is the AI application you talk to — Claude, ChatGPT, Cursor.
• The server is a service that can do or knows something — it offers the AI “tools”: search my data, save this note, read this document.
• You connect the two once, usually with an address and a short browser sign-in.

From then on the important part happens automatically: when you ask your AI a question that needs external knowledge, it calls the right tool, gets the result back, and works it into its answer. All you notice is that the answer is suddenly correct.

What does this have to do with memory?

Out of the box, every AI session is an island. The built-in memory features of ChatGPT and Claude help to a point — but they belong to the respective tool: what ChatGPT has learned about you stays in ChatGPT. Switch models and the memory stays behind.

MCP inverts that relationship. If your memory is its own MCP server, it no longer belongs to any client — it belongs to you. Every AI you connect reads and writes the same body of knowledge:

You tell Claude on Tuesday: “Remember: release moved to the 14th.” You ask ChatGPT on Friday: “When was the release again?” — and get the answer. With the source.

That’s the difference between a memory feature and memory as its own layer: models come and go, the knowledge stays in one place — searchable, linked, and exportable.

Do I need technical skills for this?

Fewer than the word “protocol” suggests. For most clients, setup takes minutes and always follows the same pattern: enter the MCP server’s address, sign in once in the browser, done. In Claude Desktop and ChatGPT the menu is called “Connectors”, in Cursor it’s one line in a config file, in Claude Code a single command.

For Memexa it looks like this — including copy-paste configuration for every client.

Frequently asked questions

Does MCP cost anything?

The protocol itself, nothing — it’s an open standard. Costs can appear on two sides: some clients only allow custom connectors on paid plans (ChatGPT, for instance), and the MCP server you connect may be a paid service.

Is it secure?

MCP itself only defines the language between client and server — how safe your data is depends on the service behind it: where it hosts, how it secures access, what it does with your content. The questions to ask any MCP service: Where does the data live? Who can read it? Can I leave any time? (Our answers: EU, only you, yes — as Markdown.)

Which clients support MCP?

As of today: Claude (Desktop, Code, Web), ChatGPT, Cursor, and a fast-growing field of other tools. Since the standard is open, practically every new AI application ships with MCP support — which means an MCP-based memory also works with the clients that don’t exist yet.

There are many MCP servers — what sets them apart?

Hundreds. Most memory servers are developer tools: locally installed, self-maintained, storing whatever the AI deems worth keeping. A hosted memory like Memexa sits one layer up: you also throw in documents, PDFs and emails, everything is read via text recognition, linked — and every answer cites its source, down to the page number.