MCP (Model Context Protocol) for Business: What It Is and When It Pays Off

What is MCP?

The Model Context Protocol (MCP) is an open standard that defines how AI applications (chat assistants, agents, developer tools) access external tools and data sources. Anthropic introduced it in late 2024; many vendors and frameworks support it now.

Before MCP, every combination of AI application and third-party system needed its own integration. With MCP, one MCP server per system (e.g. ERP, ticketing, document storage) is enough, and any MCP-capable client can use it.

How does it work?

An MCP server exposes three kinds of capabilities:

• Tools: actions the model can trigger (e.g. "create ticket", "look up invoice").
• Resources: readable content such as documents, database records or configuration.
• Prompts: predefined templates for recurring tasks.

Communication runs either locally (stdio) or over HTTP. The model decides during the conversation which tool to call; the client executes the call and returns the result.

When MCP pays off for businesses

• Several AI applications, the same systems. If a chat assistant, an agent and developer tools should all reach the same data, MCP avoids duplicate integrations.
• Agents that take actions. Once an agent system does more than return text — changing tickets, bookings or records — it needs a clearly defined tool interface.
• Swappable models. Because the tool layer is separate from the model, the choice of provider stays open.

The risks: security and data protection

An MCP server gives a model room to act. That is the benefit — and the risk:

• Prompt injection: content in documents or emails can contain instructions that push the model into unwanted tool calls. Critical actions belong behind human approval.
• Least privilege: each server gets only the permissions its use case needs — read-only where writing is not required.
• Authentication: remote servers need proper authentication (e.g. OAuth) and per-user access, not one shared superuser.
• Logging: every tool call must be traceable: who, what, when, with what result.
• Trusted sources: treat third-party MCP servers from public directories like any third-party software — review them before they touch company data.

If personal data flows to a model provider through MCP, the requirements of the revised Swiss FADP (revDSG) apply, such as data processing agreements and cross-border disclosure. We summarize them in AI and Swiss Data Protection.

Conclusion

MCP is not a hype topic but infrastructure: a shared connector between AI applications and your systems. Done right, it saves integration effort; without a permission model and logging, it creates new attack surface.