There has been a lot of hype around Model Context Protocol (MCP) lately.
If you are a Power BI Developer, Data Analyst, or Data Engineer, you might be wondering:
- Is this just another AI buzzword?
- Or is this something that will actually impact how we build data solutions?
In this blog, I’ll explain MCP in extremely simple terms, then go deeper technically so you understand why it matters, especially if you’re building AI-powered data applications.
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.
