A standard connection method can reduce integration effort. It does not reduce the importance of the systems, permissions and data behind the connection.
Model Context Protocol servers can expose enterprise tools and context to AI applications. That convenience can create a broad trust boundary if teams treat the server as a plug-in rather than production infrastructure.
What good engineering looks like
Govern the exposed capability, not only the transport. Separate read-only context from mutating tools. Validate every argument server-side. Apply user and tenant boundaries after the request reaches the tool. Monitor the full path from AI request to business change.
-
Publish a narrow tool contract with explicit side effects.
-
Use allowlists for systems, actions and data classes.
-
Require confirmation for consequential actions.
-
Return structured errors without leaking secrets.
-
Version tools and test clients against change.
A practical starting point
-
Inventory current and proposed MCP tools.
-
Mark which tools transmit or change data.
-
Map authentication and authorisation at every hop.
-
Threat-model one end-to-end tool call before production use.
The decision to make
The protocol can make access easier to implement. Architecture and governance must still make that access safe to operate.