If you have followed AI news over the past year, you have probably come across the initials MCP. Behind the technical name is a very simple idea. In practice, it has had a major effect on what AI can do inside an ordinary business. Here is the explanation without the jargon.
What is MCP in one sentence?
MCP, or Model Context Protocol, is a shared language that lets an AI model connect to external tools and systems, including a CRM, an email inbox, spreadsheets, and accounting software. Instead of building a separate connection for every model and every system, each system offers one standard socket, and any model that supports the standard can connect to it.
The most useful comparison is USB. Devices once came with different chargers. Today, one standard port works with any device that supports it.
What is an MCP server?
An MCP server is the component that sits next to your system and makes it available to the model. For example, a monday MCP server can let Claude read boards, find items, and update statuses. The server defines exactly what is allowed, including which actions are available and which data is exposed.
That matters because the AI does not receive unrestricted access. Its access is limited by what the server allows and by the permissions of the user who connected it.
Why does this matter to a business?
Until recently, using AI at work mostly meant using a chat window. You pasted in text, received an answer, and copied it back. With MCP, the model can work directly with real business information. That makes tasks like these possible:
- Ask questions about your data. Ask, “Which leads from last month never received a follow-up?” and get an answer from the CRM itself, not from an exported file.
- Make updates without typing them. After a client call, ask the model to update the status and add a note, and it does so in the system.
- Work across systems. Read an email, find the client in the CRM, and prepare a reply using the right details.
What MCP does not do
MCP does not make the model smarter, and it does not replace process design. It provides access, not judgment. If a workflow is poorly defined, connecting AI to the systems will only let it create the same mess faster.
It also does not solve security by itself. Every connection is a permission, and you need to decide deliberately what the model may read, what it may change, and which actions require human approval. Over the past year, the standard itself has moved in this direction, with support for managing permissions across an organization. That is a positive sign that it is becoming mature enough for business use.
Where should you start?
The first step is usually a read-only connection to one system, most often the CRM. A week or two of asking questions about the data reveals a great deal about what is actually useful before you grant write permissions.
Questions we get asked
Do I need a developer to use MCP? Usually not for ready-made connections. Popular tools already have MCP servers that can be connected in a few clicks. An internal or older system may require a custom server, which is development work.
Does my information go to the AI company? The information the model reads in order to answer is sent to the provider, just as it is in any chat interaction. That is why it is important to choose a business plan with suitable data terms and expose only what is required.
Does MCP only work with Claude? No. The standard started at Anthropic, but most major tools and models now support it.
What is the biggest risk? Granting broad write permissions from day one. It is better to start with read access and add actions one at a time after you have seen them work.
Want to connect AI to your CRM or other business systems in a controlled way? Talk to us, and we will help plan the first connection.
