What is the MCP (Model Context Protocol)? And how can MCP improve your AI Agent?
AI agents are becoming increasingly intelligent, but to be truly effective, they need to understand the context they’re operating in. The Model Context Protocol (MCP) is a method that defines what information an AI needs in order to perform specific tasks effectively. It brings structure, control, and relevance, the foundation for reliable AI interactions.
What does MCP actually do?
MCP acts as a kind of traffic controller for information. It defines which data should be retrieved, where it comes from, when it should be made available to the AI and when it should be forgotten.
This means the AI doesn’t need to “know everything” up front. Instead, it can dynamically access the right context at the right time, from tools like a CRM system, scheduling app, or internal knowledge base.
Why is this important?
An AI without context doesn’t know what matters. The result is often vague, generic, or even incorrect answers. Without structured access to external context, AI models operate in isolation, limiting their real-world effectiveness. MCP tackles the “M x N integration problem” by simplifying connections. Instead of M*N unique integrations, you need M clients and N servers that can all interoperate. This makes AI interactions more relevant, scalable, and grounded in up-to-date information.
MCP solves this by:
- Giving the AI only the information it needs in that specific moment,
- Connecting systems in a safe, controlled way,
- Ensuring answers remain relevant, personal, and up-to-date.
A universal connector for AI tools
MCP isn’t just about passing information, it’s about enabling seamless interoperability between AI and the tools it depends on. Whether the context lives in a CRM, HR system, email platform, or calendar app, MCP ensures the AI can speak the same “language” across systems. In that sense, MCP can become the USB Type-C port of AI ecosystems, a universal, flexible standard that allows any AI model to plug into any tool or source, securely, consistently, and with minimal friction.
Practical examples
- Customer asks for the status of their delivery
A customer sends a message: “Can you tell me where my package is?”
Thanks to MCP, the AI already knows the customer’s identity and can fetch the tracking information directly from the linked ecommerce platform. It replies:”Your package was scanned at the sorting center today at 10:34 and is scheduled for delivery tomorrow between 2:00 PM and 4:00 PM.” - Employee asks about their occupational health appointment
An employee asks: “What time is my appointment with the occupational health physician tomorrow? And can I reschedule?”
MCP ensures the AI can retrieve the relevant calendar and appointment details from the company’s absence management system. It sees the meeting is scheduled for 9:00 AM and that a reschedule request can be submitted through a linked portal. The AI responds:
“Your appointment is scheduled for Thursday at 9:00 AM. Would you like me to request a new time?” - Employee asks about remaining vacation days
An employee asks: “How many vacation days do I have left?”
MCP enables the AI to access relevant HR context from a system like AFAS. Based on contract and leave balance, the AI gives a personalized answer:
“You have 12.5 vacation days remaining this calendar year.”
The power of contextual awareness
MCP allows AI systems to work more intelligently with information. By focusing only on what’s relevant in the moment, the system stays secure, efficient, and trustworthy. No data overload. No confusion. Just answers that matter.
