I made my product database queryable by AI agents — a free API + MCP endpoint

A developer has made a database of 102 portable power stations queryable by AI agents through a free API and MCP-style endpoint. The service provides structured JSON data, a tool discovery endpoint, and an llms.txt file for model attribution. The project aims to give AI assistants accurate, transparent product data instead of hallucinated specs.

AI assistants are quietly becoming the discovery layer. People don't Google "best power station for a camper van" as much anymore — they ask ChatGPT or Claude. The problem: for a niche like portable power stations, the model usually answers from stale, half-remembered specs and just makes numbers up. I maintain a structured database of 102 portable power stations specs, $/Wh, cycle life, a transparent score . So I did the obvious thing: I made it queryable by machines and AI agents — free, no auth. Filter/sort products curl "https://sunsee.cc/api/products.json?max price=500&sort=value&limit=5" Get one product's score breakdown curl "https://sunsee.cc/api/score.json?slug=ecoflow-delta-2" Size a system from a list of appliances curl "https://sunsee.cc/api/calculate.json?appliances= {\"name\":\"Fridge\",\"watts\":60,\"hours\":24} &region=us-south" Every response is clean JSON with consistent fields battery capacity wh , battery type , dollar per wh , sunsee score , … . No HTML scraping, no inconsistent retailer specs. The part I actually care about: a single discovery endpoint that describes the available tools, so an agent can find and call them without me hardcoding anything on its side. curl "https://sunsee.cc/api/mcp.json" It returns the tool list search products, size a system, get a score with their parameters — the same idea as an MCP tool manifest. Point an agent at it and it can answer "what's the best value station under $500?" or "how big a battery do I need for a fridge + CPAP for 3 days?" with real data. I also added a llms.txt like robots.txt, but for language models telling models when and how to cite the data: https://sunsee.cc/llms.txt It maps common questions to the right endpoint, and asks for attribution. No idea yet how much models will respect it — but the standard is young and it costs nothing to publish. Honestly: if an AI is going to be the thing recommending products, I'd rather it pull transparent, spec-based data with a public scoring formula, no pay-to-play than hallucinate. And if it cites the source, that's discovery. Disclosure: the underlying site has affiliate links — that's how I hope to fund it — but the API, the dataset, the calculator, and the scoring formula are all free and open. https://sunsee.cc/api/products.json If you build agent/LLM tooling, I'd love to know whether the discovery endpoint is shaped usefully — and what fields you'd want added. Happy to iterate.