MCP and the standardisation of AI tool use

Every AI application needs to reach outside the model: to a database, a document store, a ticketing system, an internal API. For a while each team built those connections by hand, per model and per integration. The Model Context Protocol, MCP, has become the widely adopted standard for that layer, and most AI tooling now speaks it.

What it actually is

MCP is a protocol between an AI application and a server that exposes capabilities: tools it can call, resources it can read, and prompts it can use. The application does not care how the server is implemented, and the server does not care which model is calling. It is the same idea as a language server for editors, applied to AI and tools.

Why it is worth caring about

• Integrations are written once and reused across every AI application and model
• The connector layer becomes testable and versioned on its own, separate from prompts
• Swapping the underlying model does not mean rewriting how it reaches your systems
• There is a growing ecosystem of ready-made servers for common systems
• Access control and auditing have one place to live rather than being scattered through prompt code

Where it fits in a build

If you are building anything agentic, expose your internal capabilities as an MCP server rather than wiring them straight into one application. It keeps the AI layer thin, makes the integration reusable, and gives you a clean seam to test and secure. For simple single-purpose features it can be more than you need, and a direct function call is fine.

The broader point is that AI is settling into normal software architecture: interfaces, contracts, separation of concerns. MCP is one of the first pieces of that to become a genuine standard.