What Is the Model Context Protocol (MCP) and How It’s Shaping AI Development

In 2025, the Model Context Protocol (MCP) has become a leading standard in the AI wave. Developed as an open‑source protocol by Anthropic, MCP standardizes the interaction between large language models (LLMs) and external tools.

Like other protocols such as USB or HTTP, MCP provides a common language to reduce communication overhead, enabling seamless cooperation between diverse components.

MCP follows a client‑server architecture and consists of three core components:

MCP Hosts : LLM applications (e.g., Claude Desktop, Cursor) that wish to access external data via MCP.

MCP Clients : Client programs that invoke MCP Servers to retrieve external data on behalf of the hosts.

MCP Servers : Server programs that implement the MCP specification and expose data or services.

Two additional components are often involved:

Local Data Sources : Files, databases, or other resources located on the machine running the MCP Server.

Remote Services : Internet‑based data sources that the MCP Server can query, such as HTTP APIs.

Before MCP, companies like OpenAI relied on function calling, requiring developers to write separate tool integrations for each external service (weather, stock data, etc.). MCP abstracts these integrations behind a uniform interface, so adding a new service only requires deploying an MCP Server that conforms to the protocol, without changing client code.

This simplification primarily benefits developers of client applications, allowing them to connect to any MCP‑compliant service with minimal effort.