A technical framework or communication standard that enables artificial intelligence models to securely access external tools, data sources, and software environments through a structured interface, allowing models to operate beyond their internal training data. Rather than existing as isolated predictors, systems using a Model Context Protocol can query databases, read files, call APIs, or trigger actions in other applications, effectively expanding the model’s “context” in real time. This turns AI from a static text generator into a coordinated agent that can interact with live systems and workflows.
The term gained wider recognition through tooling and ecosystem efforts associated with Anthropic, which promoted MCP-style architectures to standardize how language models connect to external resources safely and predictably. In practice, MCP represents a shift from monolithic apps to composable AI infrastructure, where models act as orchestrators across many services. It is often discussed alongside agentic AI because it provides the plumbing that allows autonomous systems to actually do things rather than merely suggest them.