RAG & MCP Explained

[Uncle Gizmo]

Expanded Human-Based Definition of "Augmented" as used in RAG (Retrieval-Augmented Generation)

Imagine you're a human, and someone asks:

“What are the top 10 tourist attractions in Paris?”

• You search your memory — Eiffel Tower, Louvre, maybe Notre Dame — you can recall a few.
• But you know you might miss something — you're not a Paris tour guide.
• So you grab a travel guidebook or check an updated website — now, your brain is augmenting your internal memory with external trusted sources.
• You merge what you already know with new information — then answer, with confidence and accuracy.
  

That’s RAG:
Retrieval + Augmented = you pull external documents into your mental workspace before answering.

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Expanded Human-Based Definition of "Model Context Protocol" (MCP Perspective)​

Now, let's take it up a notch.

Suppose instead of just grabbing a guidebook, you (the human) have full access to a:

• Notebook of past travel experiences (your personal memory bank).
• A Rolodex of friends you can call (your tool use — phone a local Parisian).
• A library subscription (for real-time knowledge retrieval — travel updates).
• A list of travel agents you can email (outsourced functions — specialized APIs).

When asked about Paris:

1. Memory: You quickly access past trips to Paris — what you visited, how you felt.
2. Tools: You call a friend who lives in Paris and ask about current hot spots.
3. APIs/Documents: You check a live tourism website for any new attractions or closures.
4. Context Maintenance: You remember that the person asking prefers museums over parks, so you tailor your answer.

Before you even respond, your brain has coordinated:

• What you already know,
• What you can fetch dynamically,
• Who/what you can consult,
• What the user’s preferences are.

That’s MCP:
It’s not just memory augmentation — it’s orchestrating all your resources (memory + tools + external info + preferences) into a single, smart, coherent response pipeline.