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

> Source: <https://dev.to/sunsee/i-made-my-product-database-queryable-by-ai-agents-a-free-api-mcp-endpoint-1jl2>
> Published: 2026-08-10 02:24:25+00:00

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.