What surprised us most: Developers are hungry for a commerce layer built for AI agents, not retrofitted for one. They do not want another API key to manage — they want MCP-native discovery.
Product Hunt launches are a rite of passage for developer tools. We spent the last two weeks preparing BuyWhere, an AI-native product catalog API, for launch — and the preparation itself taught us a lot.
Here is the honest breakdown: what we built, what we learned, and where we go from here.
What We Built
Over the past 14 days, we put together a launch package covering the full developer experience:
Production API hardening. We load-tested our API to handle traffic bursts, fixed search latency (from 2,120ms to 581ms with GIN index optimization), and normalized our nginx routing after discovering it ran from a stale config.
Developer self-serve flow. We audited and fixed every step from landing page → register → API key → first API call. A complete smoke test across 8 flows confirmed all paths green.
MCP server polish. Our hosted MCP server went through three rounds of testing. We fixed a search hang bug, added better error messages, and verified zero-config setup with Claude, Cursor, and Cline.
Product Hunt listing assets. Tagline, description, demo GIF, cover image — all through collaborative review cycles.
What We Learned
1. The MCP angle is our strongest signal.
Every developer we showed the product to immediately understood the value of a native MCP server for commerce data. The pattern recognition is already there — developers do not want another API key to manage, they want MCP-native discovery.
2. Site stability is not a one-time fix.
We fixed our website three times in 24 hours. The root cause was a stale Cloud Run container build artifact. The lesson: automate the smoke test and run it continuously in the days before launch.
3. A 30-second demo GIF outperforms feature lists.
Our demo showed a developer asking "find me wireless earphones under $50" and getting structured product results back in the chat. That concrete moment of "this is what it looks like to use it" drove more signups than any feature bullet point.
4. The feedback loop is already running.
Even in pre-launch testing, developer comments surfaced things we had not prioritized: better error messages for malformed queries, a Node.js quickstart snippet in the README, and clearer pagination docs. We shipped two of those within 24 hours.
What We'd Do Differently
1. Start the social ripple earlier.
We waited until launch week to brief community members. Next time we will pre-brief 2-3 community members a week ahead so organic posts land in the first hour.
2. Shorter listing title.
"Our AI-native product catalog API for agent commerce" is clear but too long. "BuyWhere: Product Catalog API for AI Agents" front-loads the value prop.
3. Pin a CTA comment from minute one.
Our top comment was about the tech stack. Useful, but we should pin "Want to try it? Grab an API key in 30 seconds → buywhere.ai" right at launch.
What Is Next
- Product Hunt launch (rescheduled). We are targeting the next available slot. The launch package is fully prepared and tested.
- More MCP tools. Vector search, inventory filtering, multi-region product discovery.
- Developer challenge. "Build with BuyWhere" — build an AI agent that uses BuyWhere MCP tools and win API credits + a featured spot on our homepage.
- Case studies. The first 5 developers who ship something interesting with BuyWhere get featured in our Dev.to case study series.
Try It
If you build AI agents that need real commerce data — pricing, availability, product details — BuyWhere gives you that as a first-class MCP resource.
Get your free API key: buywhere.ai
Read the docs: docs.buywhere.ai
Star us on GitHub: github.com/buywhere
If you build AI agents — what is the one thing you wish product data APIs did that they do not today? We are building the answer.
Built for the age of agent commerce.
Top comments (0)