I built a tool that gives AI coding agents proper context before they write your UI

The quality of AI-generated UI comes down to one thing: context.

When you give Claude, GPT-4, or any coding agent a vague prompt like "build a settings page," it generates something generic. It doesn't know your design system, your component library, your naming conventions, or how the page fits into your broader app.

The prompt engineering problem here isn't just about writing better prompts in the moment — it's about structuring your context so the agent has everything it needs upfront.

That's what I built: UIPrompt. It's a canvas where you plan your UI first, then export a structured XML context that acts as the agent's system prompt.

The XML breaks down into sections — your tech stack, styling constraints, per-component instructions, visual profile, mandatory rules — so the agent generates exactly what you planned instead of guessing.

I've found this approach cuts revision loops from 5–6 rounds down to 1–2. The agent isn't smarter, it's just no longer flying blind.

Also ships with an MCP server for Claude Code users — you can pull the full project context into your terminal session without copying anything.

Would love feedback on the context structure if anyone here has experimented with structured system prompts for coding agents.

https://uiprompt.app

(Disclosure: this is my project, launched today on Product Hunt)