I remember the old days of manual scaling. You'd jump into a CLI, check your metrics, realize you needed another node or a capacity adjustment, log into a web console, navigate three layers deep into some proprietary dashboard, and hope you didn't click the wrong thing while trying to find a specific cluster ID.
Now we have AI agents. But most people are using them wrong. They treat Claude or Cursor as just better search engines for code, rather than giving them hands.
If you're running high-availability workloads on something like TiDB Cloud, the friction isn't in writing the SQL—you already know how to do that. The friction is in the operational visibility: knowing exactly what’s happening across your serverless instances versus your dedicated clusters without leaving your IDE.
The Gap Between Code and Infrastructure
The reason I spend so much time building things like MCPFusion is precisely because of this disconnect. An LLM might help you write a complex join perfectly, but if it doesn't know whether the target TiDB X instance is actually healthy or which project ID handles your staging environment, it's basically flying blind.
