Before we talk about what actually is Model Context Protocol (MCP) and why can it be very useful, lets first look at one of the biggest problem in AI development at the moment.
Integrating and connecting ai tools or apps to third party data sources, platforms, apps, tools and so on.
One answer to this is now quite widely used Retrieval Augmented Generation (RAG) or function calling functionalities.
But, as it turns out, engineers at Anthropic might have even better, and more universal solution to deploying in connecting appt to real-life applications.
It’s called Model Context Protocol or MCP, and it is a proposed way for AI models to reach beyond their static training.
It’s designed to give AI tools access to live data, private information, and powerful services without tying everything to a single AI provider.
The biggest problem of RAG systems is that if you switch from one model provider to another, you often have to rebuild every connection.
That is because each option has its own format and rules.
MCP aims to fix that by defining a consistent, open method of communication.
Imagine you’re coding an AI assistant that needs data from Salesforce or sends a message in Slack.
Today, you’d write custom code for each system and risk being trapped by one vendor’s tools.
With MCP, you’d set up a single “MCP server” that translates any request from your AI to the proper data source or service.
Switch AI models later, and this server remains the same.
That’s the core benefit of a universal protocol.
For a person using the AI assistant, MCP stays in the background.
You’d never see or touch it.
You’d just experience an app that fetches real-time details and updates systems more smoothly.
The difference shows up for developers and administrators, who can avoid repetitive coding and manage everything in a consistent place.
It helps to compare MCP to how React shaped web development.
Web apps existed before React, but React gave teams a shared structure and made complex tasks more straightforward.
MCP tries to do the same for AI, giving everyone a common playbook for tying AI models to outside data
Some folks also compare MCP to USB ports for AI—an easy way to plug in new “devices” without writing specialized instructions.
Whether MCP becomes a true standard depends on how many people adopt it.
Still, its vision is exciting: AI that accesses fresh data from multiple sources, no matter which provider sits behind the scenes.
That’s the promise many developers want—a simple, open, and reusable way to connect AI to the real world.