This article explains MCP in a way that doesn't require a PhD in machine learning.
The background: why MCP came about
AI models like ChatGPT, Claude, and Gemini are impressive when it comes to reasoning, writing, and analysis. But they have a fundamental problem: they live in a bubble.
An AI model doesn't know what's in your CRM. It can't pull data from your database, create a ticket in your project management tool, or check the status of your CI/CD pipeline - unless someone has built a specific integration for exactly that purpose.
And that's precisely the problem. Every time you wanted to connect an AI model to a new tool, you had to build the integration from scratch, in a way that was unique to that specific model and that specific tool. It worked, but it was inefficient and hard to scale.
MCP (Model Context Protocol) is the answer to that problem.
What is MCP?
MCP is an open protocol, created by Anthropic, that standardises how AI models communicate with external tools, data sources, and services. Think of it as a universal interface - a shared language that allows AI models to talk to virtually any tool, as long as there's an MCP server for it.
An MCP server is simply a small program that exposes a tool or data source in a way the AI model understands. It could be a server for GitHub, your database, Slack, an internal system or virtually anything else.
A simple analogy
Think of MCP like the USB standard for hardware. Before USB, every manufacturer had their own connector and their own protocol. It worked, but it was a mess. USB solved that by creating a shared interface that everyone could implement. MCP does the same thing for AI and tools.
How does it work in practice?
Imagine you have an AI agent helping your team manage customer support tickets. Without MCP, you'd need to build custom integrations against every system - your ticketing system, your knowledge base, your CRM, your calendar.
With MCP, there are instead MCP servers for each of these services. The AI agent connects to them via a standardised protocol and can directly:
• Pull information from your knowledge base to answer customer queries • Create and update tickets in your ticketing system • Look up customer data in the CRM • Book meetings in the calendar
All of this without you having to build and maintain a custom integration for each individual tool. Want to add a new tool? Connect an MCP server for it. Want to swap one tool for another? Replace the MCP server. The AI model's logic stays unchanged.
Why is everyone talking about it right now?
MCP was launched by Anthropic in late 2024 and has since gained enormous traction in the developer community. There are a couple of reasons for this:
It's open and free
MCP is an open protocol. Anyone can build an MCP server, and there's already a growing ecosystem of servers for popular tools like GitHub, Slack, Notion, Figma, Jira, and many more.
It solves a real problem
Previously, building AI agents that could actually do things in the real world was difficult. MCP removes one of the biggest obstacles and makes it possible to build agents that interact with real systems in a structured and predictable way.
The industry is rallying around it
Several major players — including OpenAI, Google, and Microsoft — have started adopting the MCP standard. This signals that it's not a passing trend but something that's on track to become an industry standard.
What does it mean for your business?
If you're building products with AI features, or exploring how AI can automate processes in your organisation, MCP is directly relevant.
In concrete terms, it means:
• AI agents you build can connect to your existing systems without massive custom integrations • It's easier to swap out or add tools without having to rewrite the AI logic • The ecosystem of available integrations is growing fast — what you need probably already exists • It becomes possible to build more sophisticated AI workflows that actually act, not just respond
It's still a relatively new standard and the ecosystem is under active development. But the direction is clear: MCP is on its way to becoming the standardised way of connecting AI to the real world.
Let's talk about it
MCP (Model Context Protocol) is an open protocol that standardises how AI models communicate with external tools and data sources. It solves a concrete problem that has long held back practical AI adoption: how to get AI to actually interact with the systems and data that already exist in an organisation.
That's why everyone's talking about it. And that's why it's worth having on your radar if you work with AI-driven product development.
Want to understand how MCP and AI agents could fit into your product or organisation? Get in touch for a free conversation.
📧 hello@redmind.se 📞 +46 08-23 08 10
Read more about our successful projects and our services.
