MCP is an open, standardized protocol for connecting an AI application to external tools and data sources, so a tool built once can be used by any MCP-compatible model or app, instead of every integration being built one-off.
Before MCP, wiring a model up to (say) your calendar meant writing custom glue code specific to that model and that calendar. MCP standardizes the connection itself: an MCP server exposes a calendar's tools in a common format, and any MCP client, whatever AI app it's part of, can discover and use them the same way.
It turns tool integrations from N-times-M custom code (every app, every tool) into a shared standard, the same reason USB replaced a drawer full of proprietary cables. A tool provider builds one MCP server; every compatible AI application can use it.
An MCP server exposes a set of tools, resources (readable data), and prompts over a standard protocol. An MCP client, typically embedded in an AI application, discovers what a server offers and lets the model invoke those tools during its normal tool-calling flow, with the protocol handling the request and response format underneath.
Because an MCP server can expose real systems, databases, file access, internal APIs, the security boundary around what an agent is permitted to call through it matters as much as the integration itself.