by Francesca Brzoskowski

Imagine this: you ask your Elasticsearch server a question in plain Dutch, such as “What were the sales figures per region last year?” and you get an immediate answer. No digging through dashboards, no writing SQL queries, no waiting days for a business analyst. That, in essence, is the promise of the Model Context Protocol (MCP).

MCP is short for Master Control Program.

MCP is a framework that connects AI tools, data, and large language models (LLMs). It standardises how an LLM accesses your data, translates that data, and performs operations on it. Fast, smart, and consistent.

MCP is not limited to just data servers. It can also work with file systems, development tools, browser automation, productivity software – in fact, anything you can link up with APIs.

Why is this important for organisations?

For managers, it's about removing friction. No more loose, ad-hoc questions that constantly take up time, but direct access to insights, even for employees without a technical background.

This means that MCP specifically offers three key benefits:

• Faster decision-making: everyone can ask questions in natural language and get immediate results.
• Less dependency: non-tech colleagues no longer have to wait for analyses.
• Better interaction: data becomes more accessible and usable for the entire team.

There is a nuance, though: as with other AI systems, the more specific and complete the input, the better the result.

Hoe werkt MCP technisch?

MCP consists of three components:

• The host This is the AI platform on which your MCP runs. This can be an internal, local environment or a model like ChatGPT or Claude.
• The clients These are the interfaces with which users ask questions, define tasks, and give commands.
• The server The tools, connectors, and data access points are stored here. The server ensures everything is available and works well together.

This division into three parts makes the framework both scalable and flexible.

Open and future-proofing

Perhaps the most beautiful aspect: MCP is open source. You are not tied to one supplier and can connect the framework with multiple tools. This makes your AI investments future-proof and prevents lock-in.

You can view MCP as an interface layer: it makes data more accessible and your teams more effective. This makes AI a practical tool for decision-making, every single day.

Conclusion

MCP servers are more than a technical innovation: they are the missing link for AI agents to collaborate more intelligently and efficiently. For organisations, this means faster decision-making, greater team autonomy, and better alignment between data and business.

Would you like to know what this could mean for your organisation? Discover more in our AI Centre of Excellence.