MCP vs. REST: What's the right way to connect AI agents to your API?

Over the past fifteen years, REST APIs have been crucial for software integration, offering a reliable, stateless interface supported by extensive tooling for developers. However, the emergence of LLM-powered agents necessitates a new approach due to REST's limitations in dynamic discovery, state management, and integration consistency. The Model Context Protocol (MCP), introduced by Anthropic, addresses these gaps by providing a JSON-RPC-based standard that allows AI applications to discover and interact with APIs through stateful sessions, without replacing REST but rather complementing it. MCP standardizes AI interactions through a consistent protocol, enabling runtime discovery, session management, and OAuth 2.1-based authentication, thus facilitating AI-native workflows that REST cannot accommodate alone. MCP servers typically wrap existing REST APIs, translating high-level MCP tools into REST calls, ensuring that REST continues to handle business logic while MCP caters to the needs of AI agents, thus offering a seamless integration layer for both traditional and AI-driven applications.