AI
MCP server development: opening your systems to AI agents
The Model Context Protocol is on its way to becoming the standard for how AI agents talk to existing systems. Whoever opens up their systems now is ready before the rest starts.
What MCP is
MCP (Model Context Protocol) is an open standard describing how an AI agent can use tools and data: which actions a system offers, which data it can provide, and how the agent calls them safely. An MCP server is the counter at which your system, your planning, your customer data, your order processing, offers itself that way.
The practical consequence: you build the exposure once, and every MCP-compatible agent or assistant can work with it. Without the standard you build a custom connector for every AI application; with MCP you build one counter for all of them.
Why this becomes the integration layer
Every vendor of AI assistants has the same problem: the model is there, but it has to reach the user's systems. MCP is the answer the major providers have lined up behind, and the supply of MCP servers is growing fast. We expect "does it have an MCP server?" to become, within a few years, the question "does it have an API?" was ten years ago.
For businesses this means something concrete: the systems reachable via MCP will take part when employees and customers work with AI assistants. The systems that are not, stand outside.
What we expose via MCP
We build MCP servers on top of existing systems: your custom application, your internal database, your business process. With the same demands we put on all our work: explicit permissions per action, a record of what an agent did, and a person at the points where one belongs. An agent that may do everything is not a tool but a risk; a well-designed MCP server makes the boundary hard.
We do not only do this for clients. Our own product in development, Stackyl, a platform for small software: building small apps with AI agents and sharing them as easily as a Google document, is driven primarily via MCP. For us the standard is not reading material but daily tooling.
Where to start
Small. One system, a handful of actions that genuinely deliver, with tight permissions. From there the counter grows with what agents turn out to need in practice. Exploring which of your systems lend themselves to this is an hour's conversation.
Related: agentic workflows on the agents themselves, and what we build for our products.
Getting your systems ready for agents?
Tell us which systems you have and what agents should be able to do there. Within two working days we think along on a sensible first step.