What is MCP (Model Context Protocol)?

A plain-English explanation of the Model Context Protocol: what it is, how clients and servers talk, what it unlocks, and why it is the foundation for memory that works across ChatGPT, Claude, and Cursor.

MCP, the Model Context Protocol, is an open standard that lets AI applications connect to external tools, data sources, and services through one common interface. Instead of every AI app inventing its own way to plug in a database, a file store, or a memory system, MCP defines a shared protocol so any compliant tool can talk to any compliant AI client.

In short: MCP is to AI assistants roughly what a USB standard is to hardware — one connector instead of a different cable for every device. That single idea is why it matters for memory that travels between tools.

TL;DR: what is the Model Context Protocol?

MCP is a standardised, language-agnostic protocol for connecting AI clients (like Claude, Cursor, or other assistants) to MCP servers that expose tools, resources, and context. The AI client speaks MCP; the server speaks MCP; neither needs custom glue code for the other. It is the integration layer that turns an isolated chatbot into an assistant that can act on real systems.

Why does MCP exist?

Before a shared protocol, every integration was bespoke. If you wanted ChatGPT to read your docs, Claude to query your database, and Cursor to reach a memory store, that was three separate, incompatible integrations — each tied to one vendor's plugin format, each rebuilt when anything changed.

That does not scale. The number of integrations grows with tools multiplied by data sources. MCP collapses that into tools plus data sources: build one MCP server, and any MCP-capable client can use it. Build one MCP client, and it can reach every MCP server. This is the same combinatorial fix standards have always provided.

How do MCP clients and servers work?

There are two roles:

• MCP client — the AI application (or the host embedding it). It initiates connections and lets the model call out through the protocol.
• MCP server — a process that exposes capabilities: tools the model can invoke, resources it can read, and prompts it can use.

An MCP server typically offers three kinds of things:

• Tools — actions the model can call, like "search memory", "create a file", or "run a query".
• Resources — readable context the model can pull in, like documents or stored records.
• Prompts — reusable templated instructions a server can supply.

When you connect a server, the client discovers what it offers and the model can use those capabilities during a conversation. The model still has no inherent memory of its own — MCP is the wiring that lets it reach systems that do. For why that distinction matters, see Why AI forgets conversations.

What does MCP unlock?

The headline use is tools: letting an assistant query systems, edit files, or call APIs through one standard. The quieter, arguably bigger unlock is portable context. Because the protocol is provider-neutral, the same MCP server can serve ChatGPT, Claude, Cursor, Perplexity, and Gemini.

That is the architecture that finally makes cross-tool memory practical. A memory system exposed as an MCP server is reachable from every MCP-capable assistant — so the decisions you made in Claude are retrievable in Cursor without copy-paste. We go deeper on that workflow value in Why cross-AI memory matters.

Why does MCP matter for memory specifically?

Memory only becomes useful when it is both persistent and reachable from wherever you are working. Persistence is a storage problem. Reachability is an integration problem — and integration is exactly what MCP standardises.

Without a protocol, a memory product would have to ship and maintain a separate integration for each AI tool, and you would have to wire each one up by hand. With MCP, a memory layer can present itself as a single server that any compliant client connects to once.

That is how Vilix is designed to work: a persistent memory layer reached through a single one-time OAuth MCP connection, working across ChatGPT, Claude, Cursor, Perplexity, Gemini, and any MCP-compatible tool. It captures full conversation context with semantic, source-aware retrieval, and you can export or delete your data at any time. MCP is the reason one connection is enough rather than one per tool.

Is MCP a model, a product, or a standard?

It is a standard — a specification for how clients and servers communicate. It is not a model and not a single company's product. Anyone can implement an MCP client or server, which is precisely what makes it useful as neutral infrastructure rather than another walled garden.

Frequently asked questions

What does MCP stand for?

MCP stands for Model Context Protocol. It is an open standard for connecting AI applications to external tools, data, and context through one common interface.

Is MCP only for Claude?

No. MCP is provider-neutral. While it originated in the Anthropic ecosystem, it is an open protocol that a range of AI clients and tools can implement, which is the point of a standard.

What is the difference between an MCP client and an MCP server?

The client is the AI application that initiates connections and lets the model call out. The server exposes capabilities — tools, resources, and prompts — that the client can use.

Does MCP give an AI memory?

Not by itself. MCP is the connection standard. Memory comes from a server that stores and retrieves context; MCP is what lets any compliant assistant reach that server.

Why is MCP important for using multiple AI tools?

Because one MCP server can serve many clients, a single integration becomes reachable from every MCP-capable tool. That is what makes shared, portable context across ChatGPT, Claude, and Cursor practical instead of bespoke.