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Let’s zoom out for a moment.
We started with large language models (LLMs) like ChatGPT. They could:
But they were limited to their training data. They couldn’t fetch live stock prices. They couldn’t query your private database.
Then we started building agent-based applications. Now LLMs could:
But to make this happen, developers had to write a lot of glue code.
Imagine you’re building an AI app that generates a stock comparison report between NVIDIA and Tesla.
The app needs to:
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.
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:
Instead of every developer writing custom integration logic, MCP defines:
Now tools expose themselves through MCP servers, and AI apps connect to them via an MCP client.
If you're a Power BI Developer, think of this like:
If you're a Data Engineer, think of this like:
If you're a Data Analyst, think of this like:
MCP is bringing semantic standardization to AI-tool interactions.
This is very similar to how:
It wraps it.
Internally:
MCP standardizes the AI interaction layer.
Think ahead:
MCP could become the standard layer between:
LLMs ↔ Enterprise Data Systems
There is hype. Yes.
But we are early. MCP has potential.
But:
Just like:
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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