The Evolution of AI Applications

Let’s zoom out for a moment.

Phase 1: Pure LLMs

We started with large language models (LLMs) like ChatGPT. They could:

• Summarize
• Generate text
• Explain concepts
• Write code

But they were limited to their training data. They couldn’t fetch live stock prices. They couldn’t query your private database.

 

Phase 2: Agentic Systems

Then we started building agent-based applications. Now LLMs could:

• Call APIs (like Yahoo Finance)
• Search the web
• Query databases
• Read PDFs
• Execute workflows

But to make this happen, developers had to write a lot of glue code.

 

What Is Glue Code? (The Hidden Pain)

 

Imagine you’re building an AI app that generates a stock comparison report between NVIDIA and Tesla.

The app needs to:

• Pull company descriptions (LLM can do this)
• Fetch latest stock price (API call)
• Retrieve financial metrics (Database/API)
• Get recent news (Web search)
• Summarize everything
• Your AI engineer builds:
• LLM at the center
• Yahoo Finance API integration
• Web search integration
• Private database integration
• Custom prompts
• Error handling
• API schema parsing

All connected through custom Python or TypeScript code.

That integration layer? That’s glue code.

Now imagine:

20 such AI apps in one company, Millions across the world

That’s a maintenance nightmare.

If Yahoo changes their API? You update code everywhere.

 

The USB-C Moment for AI

Think about old computers. You had:

 

VGA cable, HDMI, Separate charging port, Separate USB, Separate audio jack, 

Today?

Everything connects through USB-C. One standard interface.

 

MCP is the USB-C for AI applications.

Model Context Protocol (MCP) is a standardized way for LLMs to interact with:

• Tools (APIs)
• Resources (files, databases)
• Prompts

Instead of every developer writing custom integration logic, MCP defines:

• A common structure
• A common communication protocol
• A common schema

Now tools expose themselves through MCP servers, and AI apps connect to them via an MCP client.

Let’s Relate This to Data Professionals

If you're a Power BI Developer, think of this like:

• Before: Everyone builds custom connectors
• Now: Use certified connectors with standard interface

If you're a Data Engineer, think of this like:

• Before: Custom REST integration everywhere
• Now: Standardized data contract

If you're a Data Analyst, think of this like:

• Before: Everyone calculates KPIs differently
• Now: Central semantic model

MCP is bringing semantic standardization to AI-tool interactions.

 

Why This Is Powerful

Without MCP:

• Every team writes integration code
• Maintenance burden increases
• API changes break systems
• Duplicate effort everywhere

With MCP:

• Tool provider builds the MCP server
• Developers consume standardized interface
• Centralized maintenance
• Reduced glue code

This is very similar to how:

• You consume Power BI REST APIs
• You use Azure SDKs
• You rely on standard SQL interfaces

Important: MCP Does NOT Replace REST

It wraps it.

Internally:

• HTTP calls still happen
• APIs still exist
• Authentication still exists

MCP standardizes the AI interaction layer.

Why Power BI Developers Should Care

Think ahead:

• AI-powered semantic layer interaction
• AI interacting with Fabric items
• AI auto-generating reports from business language

MCP could become the standard layer between:

LLMs ↔ Enterprise Data Systems

Reality Check

There is hype. Yes.

But we are early. MCP has potential.

But:

• Adoption is still growing
• Ecosystem maturity is developing
• Governance patterns are evolving

Just like:

• Early days of Azure
• Early days of Power BI
• Early days of Lakehouse

Final Thoughts

If you're in data: You don’t need to build MCP servers tomorrow.

But you should understand the direction.

The future stack may look like:

Lakehouse → Semantic Model → MCP Server → AI Agent → Business User

MCP might become the standard bridge between enterprise data and AI.

 

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