What Is MCP?

MCP, Model Context Protocol, is an open protocol that standardizes how AI models connect to tools and data sources. Instead of building a unique connection for every system, an MCP server exposes its tools in a common way that any AI client can use. The simplest way to picture it is a USB port for AI: one connector format that everything can plug into.

The word protocol literally means an agreement on how two parties should talk. MCP is that kind of agreement between an AI model and the outside world – a shared language for how the model requests data and calls functions in other systems.

The problem before MCP

To understand why MCP is interesting, you need to see the problem it solves. An AI solution is rarely useful in isolation; it needs to reach your systems – the business system, the database, the customer registry, the calendar.

Without a standard, every such connection was built as its own small project. If you wanted to connect five systems, you got five custom-built integrations, each with its own logic to build, test, and maintain. If you switched AI tools, much of that work had to be redone, since the connections were tailor-made for the old solution specifically. It became expensive, fragile, and hard to maintain.

How an MCP server works

MCP flips the logic. Instead of every AI client building its own path into a system, the system exposes its capabilities once, according to the protocol. That’s done through an MCP server.

• The server connects to the system. It connects to, for example, a database or a business system and makes its functions available.
• The server speaks MCP. It describes its tools in the common format, so any client that understands the protocol can use them.
• The client needs no details. The AI client calls the tools without knowing the system’s internals. The server translates between the protocol and the system.

The result is that an integration built once can be used by several AI clients, instead of being rebuilt for each one.

What it means for buyers

For you as the buyer of an AI solution, MCP is fundamentally about two things: reuse and avoiding lock-in.

When integrations follow an open format, they become less tied to a single model or provider. If you switch AI clients down the line, much of the work can be reused instead of redone, which gives you both lower cost over time and more negotiating room. That’s the difference between building for a specific provider and building to be able to switch.

A concrete way to see the value: say over time you want to connect your AI solution to five internal systems. Without a standard, that becomes five custom connections, and the day you want to try a different AI tool, you effectively have to start over. With MCP, each connection is built once as an MCP server, and the same servers can then be used by a new client. The investment stays in your systems instead of in a vendor’s product.

A reasonable boundary

MCP solves how models connect to systems, not everything else. The protocol says nothing about whether an integration is secure, whether the permissions are set correctly, or whether the data may be used as you intended. Those questions remain regardless of format, and they’re at least as important. An MCP server making a system’s tools available also means you need to think through what an AI client should actually be allowed to do through it – an open door is only good as long as you know who’s allowed to walk through it.

That said, a common standard is a step toward simpler, more maintainable AI solutions. That MCP is open rather than tied to a single provider is part of the point: a format several players support has a better chance of persisting and becoming a safe foundation to build on, compared with a proprietary solution that binds you to one single party. For a buyer, it’s worth asking how the integrations are built, precisely because the answer affects how free you are to change direction later.

Want to know how your systems could be connected to an AI solution in a reusable way? Read more about our AI services or get in touch with a description of which systems need to be connected.