Developed by Anthropic—the vendor best known for Claude—the MCP (Model Context Protocol) standard has introduced an easy-to-adopt way to make AI and traditional services work together. Simply put, MCP acts as an interpreter between AI and web services. With MCP, AI does not merely answer questions; it can also retrieve and modify data, use tools, and trigger processes. This enables, among other things, asking broader and more complex questions of AI, which can then combine information from multiple data sources into results tailored to the user’s needs.
Let’s take a practical example where a user wants to know the number of open orders for the current month.
MCP does not differ significantly from traditional web services. It also consists of a server and a client through which calls can be made to the server. The key difference, however, is that the tools defined on the MCP server are not just REST interfaces—they also include metadata designed specifically for AI. This metadata helps the AI understand how, when, and for what purpose a particular MCP tool should be used.
Although MCP is a different technology compared to traditional REST architectures, systems do not need to be rebuilt from scratch. MCP tools and interfaces can be created on top of existing systems.
The flow diagram above illustrates how MCP sits between the user and the services.
When MCP is adopted, its greatest benefit is not merely technical compatibility with the many available MCP tools, but the way it makes work smoother. Instead of the user having to move between different systems to find the right information and combine it manually, AI can handle this in the background. From the end user’s perspective, the experience resembles a conversation with an expert who knows how to retrieve the right data from the right places—without the user needing to know where or how the data is obtained.
This opens up new possibilities, especially in organizations where data is distributed across multiple systems. With MCP, AI can act as a kind of universal interface to all this information. The result is not only faster access to data, but also better decision-making: when information is easier to access and combine, users can focus on what truly matters—using that information effectively.
As a case example, we built an MCP server on top of our in-house time tracking system and connected it directly for use by language models. Now users can ask about billed hours, project workload, or upcoming capacity and receive answers directly from the system without separate reporting. Reporting can also be personalized, with desired visualizations generated using language models and supporting tools based on user needs.
This example illustrates how MCP can be used to open up existing systems for AI in a controlled and business-oriented way. The same approach applies broadly across different systems and data sources. If you’d like to see how this could work in your environment, let’s talk.