Model Context Protocol is an open standard that standardizes how AI applications connect to external data sources and tools, removing the need for one-off integrations between models and systems.
Model Context Protocol defines a client-server contract for supplying context to language models. A server exposes tools, resources, and prompt templates; a client consumes them over JSON-RPC, usually over stdio or HTTP.
That single contract changes integration economics. Instead of wiring every model to every database, file store, and API separately, an application needs one MCP client and a registry of servers. Each server handles authentication, data formatting, and error handling for its own resource.
Think of it like this. Think of it like USB-C for AI integrations. One standard port replaces dozens of custom cables, and any compliant device works without special drivers.
An MCP server declares what it offers: callable tools, readable resources, and reusable prompt templates. The client discovers these at startup or through explicit listing. When the model needs data, the client sends a JSON-RPC request to the server, which returns structured results. The model never touches the underlying system directly.
Treating MCP as model-side. The protocol lives in the application layer, not inside the model. "MCP replaces all APIs." It is a presentation layer; the backend systems still need their own auth and validation. "One server for everything." Smaller focused servers are easier to secure and debug.
Reduces integration surface dramatically, but adds an intermediate layer that must be maintained. Works well for local tools and internal data; remote server deployment needs careful access control and rate limiting.