Many teams already use Claude or ChatGPT every day to write emails, summarise documents, or draft reports. But a limit appears quickly: the AI assistant does not know what is inside your company's systems. To answer a question like "how many orders are still unshipped today", staff still have to open the system, copy the data, and paste it into the chat.
This is where MCP comes in. This article explains what MCP (Model Context Protocol) is in business terms: how it works, what it is good for, and, most importantly for business leaders, how to keep it secure.
What is MCP (Model Context Protocol)
MCP is an open standard introduced by Anthropic in 2024 for connecting AI assistants to tools and data. Before this standard, every integration between an AI app and a business system had to be built separately, one at a time. MCP provides a uniform method, so a single well-built connection can be used by any AI assistant that supports it.
A common analogy is a standard plug. Just as one type of cable can serve many devices, MCP lets your systems be "plugged into" Claude, ChatGPT, or other assistants without rebuilding the integration for each one.
So how is it different from an API? APIs are still the foundation, since an MCP server usually calls your system's API behind the scenes. The difference is that MCP adds a layer AI assistants understand: a description of each tool, the parameters it needs, and a consistent result format. MCP is not a replacement for APIs; it is a way for AI to use them in an orderly fashion.
How MCP works, in simple terms
There are two sides to MCP. First, the MCP client, which is the AI app your staff use, such as Claude or ChatGPT. Second, the MCP server, a connector built in front of your company's systems, for example your order system, CRM, or document storage.
The MCP server offers a set of tools with clear names and functions, such as "find customer", "check order status", or "create quote draft". When someone asks a question, the AI assistant picks the relevant tool, the server runs it within that user's permissions, and the result goes back to the AI so it can answer in plain language.
The key point is this: the AI does not get free access to your database. It can only use the tools the server provides, within the limits you set. If a tool has not been built, the AI has no way to perform that action.
Example uses of MCP in a company
Here are some realistic examples for a mid-sized company:
- Customer service: staff ask for an order status or a customer's history directly from the AI chat, without switching apps.
- Sales: the AI pulls product and pricing data from the system to draft a quote, which staff then review.
- Operations: managers ask for a summary of low stock or overdue tasks from internal systems.
- Documents: professional teams ask the AI to search and summarise documents from specific folders only, not the entire archive.
- Finance: the AI helps compile invoices that are coming due, while payments are still made by people.
MCP security: what business leaders should insist on
Opening company systems to AI is a decision that deserves care. The good news is that MCP can be built with tight controls. When you evaluate or commission an MCP server, make sure of these four things.
Clear permissions per tool
Decide which tools only read data and which may change it, and which roles or teams may use each one. The principle is to grant the smallest access that still gets the job done.
Sign-in with OAuth
With OAuth, each user signs in with their own account, and the AI can only reach data that person is entitled to. Warehouse staff do not suddenly see salary data just because they asked through an AI assistant.
Audit logs
Every tool call should be recorded: who asked, which tool was used, and when. This record matters for tracing mistakes and for compliance.
Human approval for risky actions
High-impact actions, such as changing data, messaging customers, or creating transactions, should wait for human approval before they run. The AI prepares; a person decides.
How to get started with MCP in your company
Start with the questions your team asks most often and the systems that hold the answers. Pick one or two low-risk workflows, usually read-only ones, and test them with a small group of users. Once permissions, audit logs, and approval steps are proven to work, expand to tools that can change data. Involve your IT team or technology partner from the start, so that security is designed in from the beginning rather than patched on later. Also name an internal owner who is responsible for reviewing the audit logs regularly.
Building a secure MCP server with Averoa
If your team already uses Claude or ChatGPT and wants both to work directly with internal systems, Averoa's MCP & AI Agents service builds custom MCP servers with OAuth authentication, per-tool permissions, complete audit logs, and human approval steps for risky actions. We use the same approach in our own product, Folders, which gives Claude and ChatGPT folder-level access through MCP, with every access recorded.
