What Is MCP (Model Context Protocol)? A Plain-English Guide for Businesses

If you've been reading about AI agents, you've probably seen "MCP". It stands for Model Context Protocol, and it matters more to your business than the jargon suggests, because it changes how AI connects to the systems you already run.

MCP in one sentence

MCP is an open standard for connecting AI applications to tools and data, so an AI assistant can look things up and take actions in your systems in a consistent, controlled way.

Anthropic introduced it in November 2024 as an open specification. Within months OpenAI, Google and Microsoft had adopted it too, and it's now supported in products such as ChatGPT, Gemini, Microsoft Copilot and Visual Studio Code. It's often described as a USB-C port for AI: one standard plug instead of a different cable for every device.

In December 2025 Anthropic donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation that it co-founded with Block and OpenAI. That means no single company controls the standard, which makes it a safer thing for a business to build on.

The problem it solves

Before MCP, connecting an AI model to a tool meant writing a custom integration for that exact combination. Want the same tool available in another AI app, or with a different model? Build it again. Multiply that by every system a business uses and it gets expensive fast.

With MCP, a system is wrapped once as an MCP server that describes what it offers. Any AI application that speaks MCP can then use it.

How it works, without the code

• The AI application (an assistant, an agent, a chat tool) is where people or automations ask for things to be done.
• MCP servers sit in front of your systems, such as your CRM, a database or your file storage, and list what the AI can do with them.
• What a server offers falls into a few kinds: tools (actions, like "create a ticket"), resources (information to read, like a customer record) and prompts (ready-made instructions).

What it means for your business

• Your systems can be made available to AI once, and reused by different assistants and agents.
• You're less tied to a single AI vendor, because the connection isn't built for just one.
• Many software vendors now ship their own MCP servers, which can make integration quicker.
• It encourages clear boundaries: the server decides exactly what the AI is allowed to see and do.

What to watch out for

A standard plug doesn't make a connection safe by itself. Giving AI access to your systems deserves the same care as giving a new employee access. Ask:

• Who can it act as? Connections should use proper authentication and the narrowest permissions the task needs.
• What can it change? Separate reading from writing, and require a person's approval for anything sensitive or irreversible.
• Can we see what it did? Every call should be logged.
• Do we trust this server? Only use MCP servers from sources you trust, and review what they expose.
• Could content trick it? Text coming back from a tool, like an email or web page, can contain instructions meant to mislead the AI. Systems should treat it as data, not as orders.

Where to start

List the systems your team would most like AI to help with, and what it should be allowed to do in each. That short list is the plan for your first MCP servers. Our LLM integration work covers designing and building them securely, alongside the agents that use them.