MCP in business: connecting AI with tools safely

Model Context Protocol standardises how an AI application discovers data and functions exposed by a server. The protocol alone does not decide which actions are safe for a particular company. That requires its own access and accountability rules.

Define a specific use case

We identify the user, goal, required data and expected outcome. Reading order status has a different risk from cancelling it. Tools are designed around clear operations, rather than broad access to an entire system.

Limit data and permissions

Every connection receives the minimum scope. Read and write access are separated, as are test and production environments. Secrets remain in a protected environment, and the server verifies authority on every request.

Documentation: Model Context Protocol Specification.

Add approval for consequential actions

Financial operations, deletion, outbound messages and public publishing require a clear preview and approval of the exact target and parameters. A denial or expired authority should end safely without a partial effect.

Log outcomes, not hidden reasoning

The record includes the user, tool, target, audit-relevant parameters, authorisation decision, time and result. Sensitive data is minimised. Logs should explain a change and safely identify a retried request.

Test integration boundaries

We test missing permission, an unavailable system, an incomplete response, a concurrent-change conflict, retry and cancellation. A successful read demonstration does not prove that a write function is safe.