What Is MCP and Why Are AI Tools Suddenly Talking About It?

From Isolated Tools to Connected Intelligence

More and more AI tools are suddenly talking about MCP. In technical blogs, in changelog updates from major platforms, in conversations between developers. As a business owner you might wonder: does this concern me? Short answer: yes, because MCP changes how AI systems interact with your business data, and that has direct consequences for how automations are built.

This article explains what MCP is, why it is gaining traction so quickly, and what it means in practice when you use AI in your business.

What MCP Is, in Plain Language

MCP stands for Model Context Protocol. It is an open standard that describes how an AI model, such as Anthropic’s Claude or a custom-built AI agent, connects to external tools, databases, and services.

Before MCP, every integration was custom work. Want an AI agent to query your CRM? A developer built a separate integration. Want that same agent to check a Google Calendar or query a Supabase database? Two more separate integrations. Every connection was unique, expensive, and fragile.

MCP solves this with a single shared language. An AI model that speaks MCP can in principle connect to any system that also speaks MCP, without needing a new bridge for every combination.

Think of USB: before USB, every device had its own connector. USB created one standard that everything could plug into. MCP does the same for AI integrations.

Why This Is Moving So Fast

Anthropic’s Claude supports MCP. OpenAI has announced it will adopt the protocol. Major tools like Notion, Linear, GitHub, and Slack are building MCP integrations. Adoption is accelerating because the pain of the alternative, endless custom work for every connection, is familiar to everyone.

For businesses that use AI seriously, this means the architecture of an AI system changes fundamentally. Instead of an AI that only interacts with what has been hardcoded into it, you get an AI that can dynamically switch between tools depending on what a task requires.

A customer service AI agent can therefore not only answer questions but also look up an order in your system, schedule an appointment in a calendar, or update a customer profile, all within the same conversation. For a practical look at how that works under the hood, see the article on AI agents on your own data and how RAG works.

What MCP Does Technically

It helps to understand the components MCP consists of.

MCP Hosts

This is the environment in which the AI runs. Think of a chat interface, an automation tool like n8n, or a custom application. The host manages the connection to the AI and to the available tools.

MCP Servers

An MCP Server is a small piece of software that makes a tool or data source available to an AI. Your Supabase database can have an MCP Server. Your CRM can too. The AI does not need to know how those systems work internally, only that it can retrieve information from them or execute actions in them via MCP.

MCP Clients

The AI agent itself behaves as an MCP Client. It asks available servers what they can do, selects what is relevant for the task, and calls the appropriate action.

The result is an AI that is not rigid. It adapts based on what is available and what the situation requires.

What This Means for Your Automations

If you are currently thinking about deploying AI in your business, MCP is a concrete reason not to do that in a half-considered way.

Many businesses start with a chatbot that answers frequently asked questions. That is useful, but limited. With MCP as a foundation you build something that can keep growing: an agent that answers questions today, also saves lead information to your CRM tomorrow, and the day after automatically generates a quote when someone expresses interest.

That is also why the approach we use at NRL Automations starts with the business, not the technology. MCP is powerful, but only if you know which processes you want to connect and why. Without that clarity you build a flexible system that does not concretely contribute anywhere.

A few practical questions that help:

• Which tools do you currently use that operate in isolation from each other?
• Where do you lose time because you manually transfer information between systems?
• Which actions does your team repeat every day based on standard rules?

Those answers determine which MCP connections deliver value.

MCP and Security

A legitimate question is what MCP means for the security of your data. An AI that has access to multiple systems also has more opportunity to do something wrong.

Well-designed MCP implementations work with explicit permissions. An MCP Server specifies exactly which actions an AI is allowed to perform: read, write, or nothing at all. The AI cannot go further than what you permit. That does require that you think carefully about those boundaries during setup, and that you work with a party that takes this seriously.

This connects to something that should be at the core of every automation: security is not an add-on after the fact, but a starting point from day one.

The Reality for Dutch Business Owners

SME businesses in the Netherlands are embracing AI rapidly, but strategic application often remains superficial. MCP is an example of a development that makes the difference between AI that genuinely moves you forward and AI that only impresses in a demo.

Businesses that lay a solid foundation now, with the right integrations, the right access controls, and a clear goal for each automation, will have a system in a year that grows with them. Businesses that stack ad-hoc tools build up technical debt that is expensive to resolve later.

MCP is not hype. It is an infrastructure layer that will determine how AI systems are built in the years ahead. If you are automating now, it is smart to do that in a way that aligns with this direction.

Next Step

Want to know which integrations deliver the most value for your business and how to set that up securely? Plan a conversation and we will look together at where the biggest opportunities lie.