Chances are, unless you're already deep into AI programming, you've never heard of Model Context Protocol (MCP). But, trust me, you will.

MCP is rapidly emerging as a foundational standard for the next generation of AI-powered applications. Developed as an open standard by Anthropic in late 2024, MCP is designed to solve a core problem in the AI ecosystem: How to seamlessly and securely connect large language models (LLMs) and AI agents to the vast, ever-changing landscape of real-world data, tools, and services.

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The AI company Anthropic explained that as AI assistants and the LLMs behind them have improved, "even the most sophisticated models are constrained by their isolation from data -- trapped behind information silos and legacy systems. Every new data source requires its own custom implementation, making truly connected systems difficult to scale." 

MCP was Anthropic's answer. The company claimed it would provide a "universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol."

That's all well and good, but many companies have claimed that their universal standard would be the answer to all your technology problems. However, as the famous XKCD cartoon pointed out, if you have 14 different standards and then attempt to provide a single standard to fix everyone's problems, you'll soon have 15 different standards.

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It's not that bad with AI integration protocols, programs, and application programming interfaces (APIs), but I could see it getting that way. At the moment, the other significant MCP rivals are Google's Agent-to-Agent Protocol (A2A), workflow automation tools such as Zapier and Pica, and, of course, a variety of vendor-specific APIs and software development kits (SDKs). However, for reasons that will soon become clear, I believe MCP is the real deal and will quickly become the AI interoperability standard.

Let's get to the meat of the matter.

What is MCP?

I view MCP as a universal AI data adapter. As the AI-centric company Aisera puts it, you can think of MCP as a "USB-C port for AI." Just as USB-C standardized how we connect devices, MCP standardizes how AI models interact with external systems. To put it another way, Jim Zemlin, the Linux Foundation's executive director, described MCP as "emerging as a foundational communications layer for AI systems, akin to what HTTP did for the web."

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Specifically, MCP defines a standard protocol, built on JSON-RPC 2.0, that enables AI applications to invoke functions, fetch data, and utilize prompts from any compliant tool, database, or service through a single, secure interface.

It does this by following a client-server architecture with several key components. These are:

• Host:The AI-powered application (e.g., Claude Desktop, an Integrated Development Environment (IDE), a chatbot) that needs access to external data.
• Client:Manages a dedicated, stateful connection to a single MCP server, handling communication and capability negotiation.
• Server:Exposes specific capabilities -- tools (functions), resources (data), and prompts -- over the MCP protocol, connecting to local or remote data sources.
• Base protocol:The standardized messaging layer (JSON-RPC 2.0) ensures all components communicate reliably and securely.

This architecture transforms the "M