The AI That Listens: A story of context, connection, and MCP

In 2024, talking to AI felt like yelling into a large canyon. You’d ask a question in the  form of prompt, get a crisp, well formed answer, and that was it. No memory. No  continuity or even context. Every new session was a clean slate—as if the AI had  never met you before. It didn’t know what you asked five minutes ago, let alone five  days ago. This wasn’t because the models weren’t smart. On the contrary, they were  built on trillions of data points and trained to mimic the best of human language. But  even the most brilliant model will trip over a simple request if it lacks one key  ingredient: context—who you are, what you’ve been working on, and what your goals  really are. 

Come 2025, This is what Model Context Protocol (MCP) is changing. It is a deceptively simple innovation that’s changing how we interact with AI. It is an open  protocol that enables seamless integration between LLM applications and external  data sources and tools. More than a technical tweak, it’s a reimagining of what it  means to have a conversation with a machine. Picture this: You’ve hired a world class consultant. She’s brilliant, multilingual, and can solve nearly any problem. But  there’s a quirk—every time you speak to her, she forgets everything you’ve ever told  her. You say, “What should I write in the second paragraph?” and she responds,  “What second paragraph?” That’s what interacting with traditional large language  models felt like. Brilliant, but forgetful. Conversations were one off transactions. No  memory, no depth, no evolution. 

Think of it like handing the AI a “briefing folder” before every question. Inside: who  you are, what you’ve asked before, what you’re trying to achieve, and what matters  to you. With this, the model isn’t just answering—it’s collaborating. 

Take this example: 

• Without MCP: 

User: “Summarize yesterday’s notes.” 

AI: “I’m not sure what notes you’re referring to.” 

• With MCP: 

[MCP context provided] 

User: “Summarize yesterday’s notes.” 

AI: “Here’s a summary of your project kickoff notes from April 7: The client  approved the new timeline, and the team agreed to deliver the prototype by  April 20…” 

This isn’t magic—it’s memory. But not just memory. It’s relevant memory. MCP isn’t  about dumping everything into the model’s input; it’s about curating what matters:  your preferences, your style, your history, your goals. It is enough to help the AI be  useful, without drowning it in unnecessary details. An MCP “context packet” typically  includes several key components to help the model understand and respond  effectively. First, it contains the user profile, which identifies who is speaking,  including their tone, preferred language, and domain expertise. Next is the task intent, which clarifies what the user is truly trying to accomplish. It also includes  the interaction history, capturing what has been previously said or done in the  conversation. Lastly, it outlines any constraints and facts, such as rules or specific 

domain knowledge that the model should consider when generating responses. It’s  the difference between having a glorified autocomplete tool and a thinking partner. 

But with memory comes responsibility. MCP systems must be designed with  transparency, security, and user control at their core. Users should know what’s  being remembered—and why. Just as we expect confidentiality and professionalism  from a human assistant, we must expect the same from our digital ones. 

This protocol is already transforming how AI interacts with people across various  domains. In customer support, tools now remember ongoing issues, eliminating the  need for users to repeat themselves. Educational platforms are becoming more  adaptive, personalizing tutoring experiences based on how each student learns.  Meanwhile, business assistants can track conversations across meetings,  documents, and emails, helping teams stay aligned, informed, and more productive. 

Whether you’re using a chatbot, an AI-powered writer, or a smart assistant, context  is what turns a clever tool into a true collaborator. MCP is a foundational shift from  “respond to my input” to “understand where I’m coming from and help me move  forward.” In a world flooded with information, intelligence is no longer just about  answers—it’s about delivering the right answer, at the right time, for the right person. 

And it all began with one very human question: 

“What if the model knew what it needed to know?”