MCP is the tech term you'll be hearing all year — here's what it means

If you've been keeping an eye on the AI space lately, you've probably spotted the acronym MCP being thrown around everywhere. Tech news, developer forums, and even ChatGPT's new app integrations are all talking about the new Model Control Protocol, and for good reason.

You'll be hearing this term for quite some time. It solves a fundamental problem that has been holding back AI systems and their adoption, and that's why it's suddenly the hottest topic in artificial intelligence.

The problem MCP solves

Tech tools don’t talk to AI models well—MCP is here to change that

Think of MCP as the USB-C port for AI applications. Just like how USB-C created a universal standard for connecting a variety of different peripherals to the same port, MCP is creating a universal way for AI systems to connect with external tools, databases, and services.

Credit: Alvin Wanjala / MakeUseOf /Perplexity/Google/Wikimedia Commons

AI models like ChatGPT, Claude, or any other large language model are incredibly smart, but they're trapped within the confines of their apps or websites. They're knowledge comes from training data that usually has a cut-off date, and they can't naturally interact with external resources you'd use, such as your email or your company's database.

So every time an AI model needs to access a new data source or tool, developers need to build a custom integration. It's messy, time-consuming, doesn't scale very well, and is often specific to a particular AI model or service.

People do all sorts of things with AI chatbots, so it's reasonable to assume that they'll be connecting multiple services if given the choice. As the number of connections rises, you'll quickly get a mess of integrations that are a nightmare to manage.

What makes MCP different

It's not another ChatGPT plugin

MCP fixes this limitation by providing a standardized, open protocol that any AI application can use to connect with external systems. It's been around for a while since it was launched by Anthropic in November 2024, but adoption is now beginning to pick up.

The protocol is designed to be model-agnostic, meaning it works with any AI system, including popular ones like ChatGPT, Claude, and more. It also has a surprisingly straightforward architecture, which uses a server-client model where AI applications (the clients) can connect to MCP servers through JSON-RPC messages. The servers can expose external tools, data that the AI can read, or prompts in the form of pre-configured templates.

Credit: Model Control Protocol

For example, in the case of ChatGPT's app extensions, ChatGPT is the MCP client that accesses different apps via their respective MCP servers. This allows the chatbot to access data and the tools themselves to take action on your behalf based on the conversation you were having. You don't need to switch between apps either as everything happens in the same chat window.

The protocol's simplicity is its biggest strength. Once an MCP server exists for a service, any MCP-compatible AI app can use it. This saves developers the hassle of building custom connectors for various possible integrations.

It isn't another ChatGPT trick to make it more useful. You're going to see much deeper and expansive AI integration in a lot more apps and services powered by MCP.

If this sounds similar to features like ChatGPT's plugins, keep i mind that while the goals are simpler, ChatGPT plugins are built specifically for ChatGPT. A ChatGPT plugin for Spotify won't work with other AI models, while an MCP server for Spotify with work with any MCP-compatible AI app.

Applications for MCP are incredibly diverse. Financial companies can use MCP to let AI assistants access trading accounts and personalized investment insights, something Zerodha recently announced it's doing. Developers can use it to create AI agents that can access and work on entire codebases instead of uploading small files with code snippets to their AI conversations. Healthcare organizations can use MCP to let AI models connect to patient records and surface relevant information.

Credit: Yadullah Abidi / MakeUseOf

The roadmap for MCP is ambitious. The protocol is already working to support more sophisticated authentication mechanisms like OAuth 2.1, enterprise single sign-on integration, and a centralized MCP Registry that will function as an app store of sorts for MCP servers. There's also ongoing work on supporting multi-agent orchestration and cross-modal integration spanning text, images, audio, and video.

Why this matters for everyone

MCP can be the glue holding your AI assistant and external tools together

Even if you're not a developer, as adoption for MCP inevitably increases, it'll change how you interact with AI apps. Instead of AI agents that can only chat, we're moving towards AI agents that can book your flights, analyze your business data, manage your calendar, suggest new music and make playlists, and coordinate complex multi-step workflows—all through standardized connections that work across different AI platforms.

MCP is solving a major problem that's holding back practical AI applications. By introducing a universal standard for AI integration, it's finally letting us access the kind of seamless and intelligent automation large language models initially promised.

It's not a flashy piece of tech, and it's not something you, a user of AI services, will usually encounter. However, as it becomes the infrastructure underlying multiple AI-to-service connections, it'll become a critical part of the AI infrastructure as we know it.