MCP, or Model Context Protocol, is an open protocol that describes how large language models can obtain structured and controlled access to external data, tools and systems. The protocol defines a common way to exchange context, so an AI model can work with information that lies outside its own training data, without this integration needing to be built differently for each application.
The purpose of the Model Context Protocol is to create a clear separation between the language model itself and the resources it uses. Rather than embedding special logic directly in the model or the client, MCP acts as a standardized communication layer between the model and so-called context providers. These can provide data from, for example, files, databases, APIs, version control systems or development tools. The protocol describes how these resources are exposed, requested and returned in a predictable format.
In practice, MCP makes it possible for an AI assistant to fetch exactly the information that is relevant in a given situation. It could be source code from a repository, documentation from an internal system, or structured data from a business system. The model receives the context as a supplement to the user's prompt and can thus provide more precise, up-to-date, and situation-specific answers. At the same time, the system owner retains control over which data are available and how they may be used.
An important property of the Model Context Protocol is that it is designed to be provider- and platform-independent. This means that the same MCP server can be used by different clients and models, and that new tools can be added without changing the underlying architecture. This approach is reminiscent of well-known standards in software development, where fixed protocols reduce complexity and promote reuse.
MCP plays a central role in the development of more advanced AI systems that are to function as practical assistants rather than isolated text generators. By giving models structured access to external context, the protocol supports use cases such as code assistance, knowledge seeking, automation, and analysis across systems. The Model Context Protocol can thus be seen as an important building block in the transition from general language models to integrated, context-aware AI solutions.