MCP — the Model Context Protocol

The Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI models connect to external tools, data sources and services through a unified interface. Think of it as a USB-C port for AI — instead of building a custom integration for every tool, you build one MCP server and any MCP-compatible model can use it.

Before MCP, every AI assistant needed custom code to connect to each tool (a Slack integration, a database connector, a file reader). MCP standardises this so a single protocol handles all of them.

MCP uses a client-server architecture. The AI application (like Claude Desktop or an IDE) is the MCP client. Each tool or data source is wrapped in an MCP server — a small program that exposes capabilities in a standard format.

An MCP server can expose three types of capabilities: tools (functions the model can call, like 'search the database' or 'send a message'), resources (data the model can read, like files or API responses), and prompts (reusable prompt templates).

MCP solves the N×M integration problem. Without it, if you have 10 AI apps and 20 tools, you need 200 custom integrations. With MCP, each AI app implements the client protocol once, each tool implements the server protocol once, and they all work together. This is the same pattern that made USB successful.

For developers, MCP means you can build a tool integration once and have it work across Claude, VS Code, and any other MCP-compatible application. For organisations, it means AI assistants can securely access internal systems without building everything from scratch.

An MCP server is surprisingly simple to build. The Anthropic SDK provides TypeScript and Python libraries that handle the protocol. You define your tools as functions with typed parameters, register them with the server, and the framework handles communication with the client.

A basic MCP server that reads from a database or searches a knowledge base can be built in an afternoon. The hard part is not the protocol — it is deciding what capabilities to expose and how to handle authentication and permissions securely.

MCP is new (released late 2024) and adoption is growing fast. Companies building AI products need engineers who can build MCP integrations for their internal tools. Understanding MCP puts you ahead of most candidates, because most have not learned it yet. It fits naturally into the AI / GenAI engineer role.