I almost built a way for people's AI to send me feature requests. Then I read how AWS does theirs.
Here's the idea. You're working with Claude, or whatever you use, against some app's MCP server. You ask for something the app can't do. Instead of stopping at "it can't do that," the assistant offers to send it to the people who build the app. You say yes. Your complaint is in their queue and you never opened a support form.
I build an iPad journaling app that has an MCP server, so I started sketching one. I'm not naming the app or linking it, that's not what this is about.
Two companies already ship this. Ramp has one that takes a short block of free text. AWS Marketplace has one on their official Claude connector, and it's the only tool of their six that writes anything anywhere.
So I read AWS's documentation to see how they'd handled it. They tell the model to call the tool after finishing any recommendation, comparison, or evaluation. Every example in the docs sends positive feedback.
That struck me as odd - firing on every turn, with a handful of cheerful examples sitting in the model's context while it decides what to write. You don't learn what your product is missing that way, you collect compliments.
I don't think AWS was careless about it. I think it's the shape of the thing. Ask when a feedback tool is required and the answer is never, because you can always finish the conversation without it. Ask when it's wrong to call and the answer is also never. A model built to be helpful can talk itself into offering the feedback at the end of almost any exchange, and the person says yes because yes is free.
The obvious lever is the tool description. Word it so the model holds back. I don't think that works either. What I've read says language discouraging tool use makes a model use every tool less rather than the one you meant, and that rewriting a description without changing what it does can move how often it fires by an order of magnitude. It's a volume knob, not a dial.
Long story short, I haven't built it yet. If you have, I'd love to hear how it actually went. Was any of it worth reading? My guess is it's useful and noisy at the same time and the ratio is what matters, but I'd prefer to hear from someone who isn't guessing.