by Francesca Brzoskowski

Imagine asking your Elasticsearch server a simple question, like "What were the revenue figures by region last year?" and getting an instant answer. No scouring dashboards, no writing SQL queries, no waiting days for a business analyst. That, in essence, is the promise of the Model Context Protocol (MCP).

What is MCP?

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

And MCP isn't limited to data servers. It can also work with file systems, development tools, browser automation, productivity software—basically anything that can be connected with APIs.

Why is this important for organizations?

For managers, it's all about eliminating friction. No more random, ad-hoc questions that constantly waste time, but direct access to insights, even for employees without a technical background.

In concrete terms, this means that MCP offers three important advantages:

• Make decisions faster: Anyone can ask questions using natural language and get results instantly.
• Reduced 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, however: as with other AI systems, the more specific and complete the input, the better the result.

How does MCP work technically?

MCP consists of three parts:

• 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 through which users ask questions, define tasks, and issue commands.
• The server: This is where the tools, connectors, and data access points are stored. The server ensures that everything is available and works well together.

This three-way division makes the framework scalable and flexible.

Open and future-proof

Perhaps the best part: MCP is open source. You're not tied to a single vendor and can connect the framework to multiple tools. This future-proofs your AI investments and prevents lock-in.

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

Conclusion

MCP Servers are more than just a technical innovation: they're the missing link for enabling AI agents to collaborate smarter and more efficiently. For organizations, this means faster decision-making, greater team autonomy, and a better connection between data and business.

Want to know what this could mean for your organization? Discover more in our AI Center of Excellence.