Overview

Artificial intelligence has reached an inflection point. The past few years have delivered foundation models of astonishing capability-systems that can write essays, generate images, reason through mathematics, and converse with nuance that was unimaginable a decade ago. Yet for all their power, these models remain strangely isolated. They live inside walled gardens of inference servers, receiving text prompts and returning text responses, largely blind to the broader digital world they are meant to transform. When an AI needs to check a calendar, query a corporate database, send an email, or even open a spreadsheet, the connection is usually improvised, fragile, and ad-hoc. Context is lost the moment a new tool is invoked. State is forgotten between turns. Security boundaries are crossed through brittle workarounds. The result is a landscape of clever demonstrations that rarely survive contact with real production requirements. This fragmentation is not merely inconvenient; it is the single largest barrier preventing artificial intelligence from becoming a ubiquitous operating layer for modern software. Every enterprise that attempts to deploy AI at scale quickly discovers the same painful truth: raw model intelligence is abundant, but reliable, repeatable, and secure interaction between models and the rest of the software ecosystem remains scarce. The Model Context Protocol (MCP) was created to solve precisely this problem. At its core, MCP is an open communication standard designed from first principles to let language models-or any reasoning engine-interact with external tools, data sources, and each other in a structured, stateful, and context-preserving way. It is not another wrapper around existing APIs, nor is it a high-level orchestration framework that hides complexity. Instead, MCP establishes a thin but rigorously defined layer that sits between models and the world, providing the missing plumbing that production-grade AI systems have desperately needed.

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