# Lux Stay Agent — a hotel travel agent that only works because its content is structured

> Source: <https://dev.to/jeffreyturov/lux-stay-agent-a-hotel-travel-agent-that-only-works-because-its-content-is-structured-5ff6>
> Published: 2026-09-18 21:24:36+00:00

I run a real 3-star hotel next to Luxembourg's central station. For the Sanity Challenge, instead of building a demo on fake data, I pointed a production AI agent at a Sanity Knowledge Base filled with the hotel's **actual operational content** — live room prices, the real 54-dish snack menu, transport facts, multilingual FAQs. The result: **Lux Stay Agent**, a travel assistant that answers travelers' questions (FR/EN/DE) with exact figures it cannot afford to get wrong, and that would be completely useless without structured content.

**Live demo (real recorded sessions):** [https://yasha.phoenix--ai.com/agent/](https://yasha.phoenix--ai.com/agent/)

**Sanity project ID:** `vxozr96i` (dataset `production`)

"How much is a double room?" sounds trivial until you realize the price changes daily (our rates sync with Booking.com every night). "How do I get from the airport?" has one correct answer (bus 16, free — Luxembourg made all public transport free in 2020). "What's on the menu?" is 54 items with individual prices living in the hotel's production ordering system.

A keyword search over a website gets you approximate, stale, or wrong answers. An LLM without grounding gets you confident nonsense. What works is an agent that *queries* structured content — exact fields, exact numbers — through a scoped, read-only window.

I modeled the hotel's world as six Sanity document types:

| Type | Content | Why it must be structured | 
|---|---|---|
| `hotel` | Address, phone, check-in/out times, distances (100 m to station, 400 m to center) | Exact facts, zero tolerance for approximation | 
| `room` | 3 types, capacity, bed, size, **basePriceEur** + price-sync note | A price is a number field, not prose | 
| `menuItem` | 54 dishes, category, **priceEur** , availability, ordering note | Pulled from the live ordering system (RoomEats) | 
| `guide` | Airport/station/transport guides with `facts[]` arrays | Bus lines, durations, the free-transport rule | 
| `faq` | FR/EN/DE questions & answers | The agent answers in the user's language | 
| `attraction` | Sights with **walkingMinutesFromHotel** , UNESCO flags | "What can I visit on foot?" is a numeric query | 

Every document traces back to a real system: the booking engine I built for the hotel (rates synced nightly from Booking.com), the production QR-ordering database for the menu, and verified local transport facts.

```
Traveler question (FR/EN/DE)
        │
        ▼
Lux Stay Agent (Python, OpenAI-compatible LLM, function calling)
        │  1. fetches /initial-context over HTTP → schema-aware system prompt
        ▼
Sanity Context MCP endpoint
https://api.sanity.io/v2026-03-03/context/mcp/vxozr96i/production?embeddings=true
        │  tools: schema_explorer, groq_query, array_field_reader
        ▼
Sanity Content Lake — 78 real documents, embeddings enabled
        (semantic search via text::semanticSimilarity)
```

The agent loop is deliberately boring — that's the point. The intelligence lives in the content model:

``` php
def run_agent(question):
    tools = get_tools()  # MCP tools/list -> OpenAI function schemas
    messages = [
        {"role": "system", "content": SYSTEM.replace("{ctx}", initial_context_http())},
        {"role": "user", "content": question},
    ]
    for _ in range(MAX_ROUNDS):
        msg = llm(messages, tools)
        if not msg.get("tool_calls"):
            return msg["content"]           # grounded final answer
        for call in msg["tool_calls"]:
            result = mcp_tool(call["function"]["name"],
                              json.loads(call["function"]["arguments"]))
            messages.append({"role": "tool", "tool_call_id": call["id"],
                             "content": result})
```

The system prompt is strict: exact figures only via `groq_query`, never from memory; cite the source document type; if the base doesn't know, say so and hand over to the hotel's phone/email; answer in the user's language.

Three moments from real sessions:

**"Combien coûte une nuit en chambre double et à quelle distance de la gare ?"**

The agent fires one `groq_query` on `room` (`basePriceEur: 95`) and one on `hotel` (`distanceToStationM: 100`). A keyword search would find a page mentioning "double room" and "station" — it would not reliably bind 95 € to *this* room type on *this* date.

**"Quels plats avec du kebab, et à quel prix ?"**

The menu is 54 `menuItem` documents with `name` and `priceEur` as queryable fields. The agent answers with the exact list and prices — and surfaces the pricing contradiction honestly: each item carries the Wolt delivery price *and* the note that ordering in-room via QR is 15–25% cheaper. Both claims, with their sources, side by side — exactly what structured content enables.

**"Was kostet ein Einzelzimmer und wie komme ich vom Flughafen zum Hotel?"**

German question → German answer, because the FAQ documents are tagged `language: "de"`, while the room price still comes from the language-neutral numeric field. Translation happens at the LLM layer; facts stay exact at the content layer.

Before writing the agent, I ran a 15-assertion QA battery against the Context MCP endpoint itself: schema visibility for all 6 types, exact price queries, reference resolution (`room.hotel->name`), document counts (54 menu items), guide facts, semantic search with embeddings, and multilingual retrieval. **15/15.** Then a 5-question battery against the full agent loop (German, menu prices, UNESCO sights, English, free transport) asserting exact figures in the answers and actual tool usage. **5/5.** The demo page replays these unedited sessions.

`/initial-context` over HTTP is a real optimization`embeddings=true`) but needed enabling per-dataset` sanity datasets embeddings enable`) — the error message was clear, the fix took a minute with the CLI once I had a token with the right grant (`deploy-studio` role for hosting, `developer` for embeddings).`walkingMinutesFromHotel` as a number turns "what can I visit?" into a sortable query. That modeling decision is the whole game.`vxozr96i`)
If you have content an agent can't afford to get wrong — prices, inventory, schedules, errata — structure it, scope it, and let the MCP endpoint do the rest. The agent is the easy part.