The Model Context Protocol (MCP) and REST API are two different ways to build generative AI applications. MCP is starting to gain attention as a more appealing method for interacting with AI applications. We talked with Sergii Bataiev, Director of Architecture and Technology at ELEKS, about how MCP works, what makes it better than REST APIs, and what it could mean for the future of app development and user experience.
What is the MCP, and how does it differ from REST APIs?
The Model Context Protocol (MCP) represents a paradigm shift in how AI systems interact with applications, offering advantages over traditional REST APIs. While REST APIs have effectively served web development through their client-server architecture and stateless design, they fall short when facilitating the complex, context-aware interactions that modern AI systems require. MCP elegantly solves the "N×M problem" that plagues REST implementations, where each combination of model and tool demands custom integration, by establishing a standardised communication layer specifically designed for AI-application interactions.
What makes MCP transformative for AI-application interactions?
What makes MCP transformative is its foundation in bidirectional communication and dynamic tool discovery. Unlike REST's design, MCP preserves conversational context and allows AI agents to discover capabilities at runtime rather than design time. This enables a future where AI agents can serve as the primary interface between users and digital systems–managing emails, calendars, databases, and applications through natural language instructions rather than traditional GUI interactions. The protocol's ability to maintain state throughout interactions creates truly conversational experiences.
What impact does MCP have on application development practices?
Looking forward, MCP promises to fundamentally alter application architecture and development practices. We're already witnessing the emergence of applications designed to be "AI-comprehensible" from inception, with data structured specifically for context-aware AI consumption. The protocol enables a composable tool ecosystem where capabilities can be dynamically orchestrated by intelligent agents according to specific task requirements.
What challenges must be addressed for widespread MCP adoption?
While MCP clearly represents the foundation for the next generation of AI-integrated systems that will transform how users interact with technology, adoption challenges remain, particularly around security, privacy, and standardisation.
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