Recently, a friend's experience with AI highlighted an evolution in artificial intelligence technology that I believe deserves attention. Her story perfectly illustrates how the new Model Context Protocol (MCP) - introduced in November 2024 - can transform day-to-day AI from merely clever to genuinely useful.
My friend had heard about Gen AI's capabilities and decided to test it out for her job search. She uploaded her resume - naming it after a fictional character - to a standard Large Language Model, hoping it would match her experience with current job openings.
As can be seen, she hit this wall when her Gen AI client (Anthropic's Claude is this example) responded with an apologetic message explaining it couldn't access current job listings. While it could analyse her resume, it couldn't connect to actual current opportunities, limiting its practical value.
Although a tailored AI program could be developed to meet my friend's needs, she wanted to carry out the task using the standard (in this example) Anthropic's Claude user interface.
Enter MCP - a game-changer in how AI systems interact with real-world data - whether locally or via web services. When she tried again with an appropriately MCP-enabled system, the difference was striking...
Not only could the AI analyse her resume, but it immediately connected to current job databases, delivering specific, relevant opportunities, and provided the additional, actionable analysis that she originally requested...
Think of MCP as a universal translator between AI and real-world data sources. Here's what makes it so powerful:
Large Language Model Agnostic: MCP is a protocol that could interface with any LLM. The above example uses Anthropic's Claude. As long as MCP servers and clients follow the protocol, they could be used by a broad range of suppliers' products.
Real-Time Connection: Instead of working with static, outdated information, MCP allows AI to tap into live data streams.
Contextual Understanding: MCP helps AI understand not just what data exists, but how to use it meaningfully. In my friend's case, it didn't just match keywords - it understood the context of her experience and how it related to current market demands.
Practical Action: Perhaps most importantly, MCP transforms AI from an advisor to an enabler. Rather than just suggesting what might be possible, it can actually help execute tasks using real-world data and systems.
Security and Control: Crucially, MCP incorporates robust security protocols, ensuring that AI systems can only access authorised data sources with appropriate permissions. Organisations maintain full control over what information their AI systems can access and how they can use it.
The implications extend far beyond job searching.
Whether it's checking real-time inventory for shopping, accessing current medical research for healthcare decisions, or analysing live market data for investment choices, MCP is making AI truly practical and immediately valuable.
Developed by Anthropic, MCP represents a significant step forward in AI integration. Importantly, it's designed to be model-independent, meaning it can work with any AI system that adheres to its protocols, regardless of the underlying technology or provider. This universal compatibility ensures that organisations can implement MCP across their existing AI infrastructure without being tied to specific vendors or platforms.
It's no longer about what AI knows - it's about what AI can actually do. And with MCP, it can do a lot more than we might have imagined.
Jean doesn't exist, the resume was synthetic. But MCP is real and so are the potential benefits.
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