MCP: The Missing Layer Between Your App and the AI

Last year, building AI into your app meant prompt engineering.
This year? It’s all about context engineering.

There’s a quiet shift happening in how we design AI-powered products. It’s not just about what the model can do — it’s about what the model knows before you even send the prompt. That’s where Model Context Protocol (MCP) comes in.

If you’ve never heard of MCP, don’t worry. You’re early. Really early.

But you won’t be for long.

What is MCP, really?

At a high level, MCP is a spec — spearheaded in part by OpenAI — that lets developers pass structured, persistent context to large language models. Think of it as an evolving memory layer: a format that describes who your user is, what they’ve done, and what matters to them, all in a clean, machine-readable way.

Instead of rewriting a user’s story with every new prompt, you give the model an up-to-date snapshot of that story in advance.

A meal-tracking app today might prompt:

“Summarize: 1 avocado toast, iced latte, 2 boiled eggs.”

With MCP, it might instead include something like:

{
  "user_profile": {
    "name": "Stephen",
    "goals": {
      "protein_target_g": 140,
      "meal_frequency": "3/day"
    },
    "patterns": {
      "morning_preference": "low-effort, high-fat meals",
      "tracking_style": "minimal"
    }
  },
  "meal_input": "Avocado toast, iced latte, 2 boiled eggs"
}

The result? A more accurate, contextualized analysis. The AI isn't guessing in a vacuum. It knows you.

Why is this important?

Right now, most AI-powered apps suffer from short-term memory. Each time the model is invoked, the prompt has to do everything. There's no persistence, no nuance, no actual learning happening.

This makes for cool demos, but shallow products.

Prompt engineering is like writing one-off sentences.
MCP is about writing a story arc the model can follow.

If you’ve ever wondered why so many AI apps feel robotic or generic — this is why. Without context, even the smartest model becomes a goldfish.

The future of AI is not just about higher-quality models. It’s about deeper context. Personalization. Relevance. Memory.

What makes MCP different?

A few things stand out:

• It’s model-agnostic. While OpenAI is an early leader, the protocol is designed to work with other models and services. It could unify how apps handle memory across multiple backends — even local models.
• It separates prompts from context. Instead of jamming everything into a giant input string, you maintain a structured context object that can evolve independently.
• It encourages reusable patterns. By formalizing context, devs can create modules that are composable and easier to reason about. Imagine context-aware middleware.
• It elevates the role of product design. You’re not just passing data — you’re making decisions about what the model should know and how long it should remember it.

These are design questions, not just engineering ones.

Context is a Product Problem

Once you start thinking in MCP terms, you stop treating AI as a magic box — and start treating it like a teammate.

You begin asking questions like:

• What should the AI remember between sessions?
• When is it helpful for the model to forget something?
• How do we make this experience feel familiar… but not creepy?

These are human-centered decisions.

The analogy I keep coming back to is a good therapist. The best therapists don’t just ask great questions — they remember what you told them last week. They connect the dots over time. They help you see patterns you didn’t notice yourself.

That’s what the best AI-powered apps will feel like in a few years. Not flashy. Not performative. Just deeply tuned to you.

Real-World Use Cases (That Aren’t Hype)

Let’s move from abstract to real.

Here are three concrete examples where MCP-style context design would meaningfully improve AI experiences:

1. A Fitness App That Actually Knows You

Instead of:

“Generate a 7-day workout plan for a beginner.”

With MCP:

{
  "user_profile": {
    "goals": ["fat loss", "core strength"],
    "equipment": ["dumbbells", "pull-up bar"],
    "injury_history": ["knee strain"],
    "available_time": "30 mins/day",
    "training_style": "solo"
  }
}

This plan won’t just be “for a beginner” — it’ll be for you, factoring in history, context, and even evolving goals over time.

2. A Journaling App That Connects the Dots

Instead of:

“Summarize today’s journal entry.”

With MCP:

• Knows your mood trends
• Tracks recurring themes (e.g. work anxiety, motivation dips)
• Surfaces insights across entries over time

Imagine an entry that begins with:

“You’ve written about burnout 3 times this week. Want to explore that?”

3. A Messaging Assistant That Knows Your Tone

Instead of:

“Draft a reply to this email.”

With MCP:

• Knows your writing style
• Understands your relationship with the sender
• Adjusts based on tone, urgency, and past responses

“This sounds like a message from your teammate Dan. You usually keep it informal but solution-oriented. Here’s a suggested response…”

How I’m Applying This

When I started building AteIQ, I didn’t just want an AI feature — I wanted an AI-native product. That meant rethinking the foundation.

From the beginning, AteIQ was designed with an MCP-first mindset. Not just “what did the user enter?”, but “what should the model understand about them over time?” That thinking shaped how the core systems were built — from how meals are interpreted to how user intent is tracked and reinforced.

As the roadmap unfolds, MCP-inspired functionality will become more visible. Some of it’s already quietly working under the hood. Other parts will surface soon — in ways that make the app feel more personal, more contextual, and more aligned with the user’s evolving goals.

AteIQ doesn’t just use AI. It models the person using it.

If I Were Starting From Scratch…

Every time I explore what MCP unlocks, I come up with new product ideas — some weird, some wildly promising.

It’s hard not to get excited. MCP flips the default on how we build: instead of asking, what can this model do?, we start asking, what should it already know?

That’s a radically different foundation for apps — and it opens the door to a new class of experiences that feel truly adaptive, even empathetic.

I’ve got a few of these ideas brewing — some even outlined in far too much detail — but right now, my focus is on evolving AteIQ and pushing what AI can do in a real, shipped product.

Let’s just say: MCP changes what’s possible.

So What’s Next?

Right now, MCP is still evolving. OpenAI hasn’t published the full protocol yet. But the writing is on the wall. The future of AI apps is context-first.

I’m building in that direction, learning as I go, and sharing what I find. Not because I have all the answers — but because I know this matters.

If you’re exploring this space too — hit me up. Let’s talk design patterns. Let’s trade prompts. Let’s experiment.

This isn’t just technical progress.
It’s a design shift.
A new discipline.
A new interface layer between humans and intelligence.

And maybe — just maybe — this is the protocol that makes AI feel human.

This post explored how MCP can transform AI apps from reactive tools into deeply contextual teammates. I’ll be sharing more — from design strategies to real-world experiments inside AteIQ.

If you’re exploring this space too, I’d genuinely love to connect or you can drop me a line.
And if you’re just getting started, I hope this blog becomes a place you can revisit and grow alongside.

Until next time — structure that context.