The problem MCP solves
A language model only knows its training knowledge and whatever is in the current conversation by default – not your customer data, your ticketing system, or your accounting. Before MCP, every connection between a specific AI app and a specific tool needed its own, custom-built integration. With M AI providers and N tools, that produces M×N separate connections – each maintained individually.
How MCP works, technically
MCP is an open standard (published by Anthropic, now supported by several providers) for exactly one shared language instead of M×N individual solutions. The AI app (the "host," e.g. Claude Desktop) contains an MCP client that connects to one or more MCP servers. Each server offers three building blocks: tools (executable functions, e.g. "cancel an order"), resources (readable data, e.g. a customer record), and prompts (ready-made templates for recurring tasks). Communication runs over a standardized message format – locally, with the server running as a subprocess on the same machine, or remotely over an internet connection, when the server runs on its own machine or in the cloud.
MCP vs. a classic API integration
With a classic integration, someone writes code for exactly one combination of AI provider and tool. An MCP server built once, by contrast, works in principle with any MCP-capable AI client – no longer 1:1, but 1:many. That cuts effort substantially, especially once several tools need to connect to several AI clients.
Where MCP hits its limits
A locally running MCP server doesn't automatically mean local AI: the data actually requested still goes to the AI provider's cloud for processing – MCP is an access path, not a privacy mechanism. And the more an MCP server offers executable tools rather than just readable resources, the more it matters that there's a real approval step before every action (see "Human in the loop: when AI needs approval" and "Security in production use") – MCP itself doesn't enforce that; the connecting app has to implement it.
Why this matters for you as a decision-maker
MCP shifts an older either-or question: does your own software have to connect to an AI through the paid API, or is an existing subscription enough? For the case of "a person operates their own software through their own AI client," the answer is often the latter – details, limits, and the legal framing (individuals, companies, products) are covered in the article "AI in your own software – via MCP and your subscription, without paying for the API."