Model Context Protocol (MCP): A Primer
Model Context Protocol (MCP) is a framework that standardises how AI models represent and process context, enabling more consistent and predictable interactions across different systems.
At its core, MCP defines structured formats that help maintain coherence between prompts, responses and the overall context window.
The protocol establishes clear conventions for how context should be formatted, tagged, and interpreted by large language models.
MCP is gaining attention because it addresses a critical problem in AI deployment: the inconsistent ways different systems handle context, which leads to unpredictable outputs and integration challenges.
The protocol’s sudden rise in popularity stems from the growing ecosystem of AI applications that need to communicate seamlessly with each other.
As organisations deploy multiple AI models simultaneously, MCP offers a solution to the “Tower of Babel” problem where each model speaks its own contextual language.
The business value of MCP comes from reducing development time and costs when building AI applications that need to work across multiple models or platforms.
Major AI providers are embracing MCP as a way to create more interoperable systems that can easily exchange context information without losing meaning.
The protocol’s recent surge in attention coincides with the enterprise shift from experimental AI to production-grade systems that require standardisation and reliability.
MCP represents a natural evolution in AI development similar to how HTTP standardised web communications, offering a common language for context that helps models understand each other more effectively.
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Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
