Understanding the Model Context Protocol (MCP): A Friendly Guide

Education
July 31, 2025

5 minutes read

What Is the Model Context Protocol?

The Model Context Protocol, or MCP, is a set of rules and structures that let AI models access, retrieve, and update contextual information about a user or an environment across sessions and tools. Think of it as memory for AI, but with privacy, structure, and purpose.

Just like you remember your friend’s favorite coffee order or your past conversations, MCP enables AI to do the same. It makes interactions smarter, more useful, and more human.

Why It Matters

Most AI tools today are stateless. When you start a new session, the AI forgets everything. You need to remind it who you are, what you like, or what task you were working on.

MCP changes that.

With MCP, your AI assistant can:

  • Recall your preferences
  • Understand past actions
  • Pick up projects where you left off
  • Integrate with tools like email, calendars, or CRMs

This opens the door to true personal AI — one that doesn’t just answer but assists.

How It Works

MCP acts as a middle layer between your AI model and your data. Here’s a simplified structure:

  1. Memory Engine — a store of relevant facts, summaries, and personal information.
  2. Context Window — the short-term memory fed into the model at every prompt.
  3. Retriever — finds the right data from the memory engine to include.
  4. Updater — adds new insights or facts to the memory based on interactions.

This architecture allows the AI to be contextually aware, improving over time.

Examples of MCP in Action

Let’s look at three AI tools and projects that are already implementing MCP (or a close version of it):

1. Cognee + Claude (by Anthropic)

Cognee has built a powerful memory engine that connects to Claude using MCP. It can plug into your GitHub, Slack, or Google Drive, giving Claude real-time memory of your activity across tools.

  • Visit Cognee

2. FuseBase (formerly Nimbus)

FuseBase enables businesses to deploy AI agents that remember tasks, team details, and workflows using MCP principles. These agents work across client portals, internal tools, and more.

  • Visit FuseBase

3. Memoripy (Open-Source Tool)

Memoripy is a Python library that adds structured memory to any AI agent. It clusters, decays, and reinforces memory entries, making it ideal for long-term conversational agents.

Why Startups and Developers Should Pay Attention

MCP is more than just a feature. It’s a fundamental capability for any AI product that wants to be personal, intelligent, and scalable. In the near future, we can expect MCP (or similar systems) to be the standard for:

  • Personal AI assistants
  • Enterprise chatbots
  • AI copilots for design, code, and content
  • Smart home hubs

If your startup is building AI interfaces, understanding and adopting MCP early is a huge competitive advantage.

Final Thoughts

The Model Context Protocol is the brain behind long-term AI intelligence. It turns short-term memory into enduring understanding. With tools like Cognee, FuseBase, and Memoripy leading the way, we’re getting closer to a world where AI is more like a trusted partner than a search box.

Want to build AI that remembers? Start by learning more about MCP.

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