Trip Scout — an agentic travel planner for Alexa+

"Alexa, plan a long weekend in Barcelona in November — we have $2,500."

"Best value: fly Friday, November 13, back Monday the 16th, and stay at Casa Jam, rated 4.7. $1,394 in total — $1,106 under your budget. Want me to watch this price?"

Trip Scout is a self-hosted MCP server (spec 2025-11-25, Streamable HTTP) plus an Agent Skill that gives Alexa+ a real travel planner. One spoken request fans out into dozens of live Google Flights and Google Hotels searches, and comes back as one sentence for the ear and an MCP Apps card for the Echo Show screen.

It is not a wrapper around one API. It is an agentic workflow:

1. compares round-trip fares across a whole travel window (up to 14 departure days, in parallel),

2. takes the three cheapest dates and pulls hotels for exactly those nights,

3. filters hotels by the traveler's standards (review-weighted rating, stars, no dorm beds for couples),

4. builds and ranks complete flight + hotel packages against the budget,

5. remembers the traveler (home airport, party size, cabin, nonstop) and watches prices across sessions.

Try it in 60 seconds

git clone https://github.com/janik4321sdfa/trip-scout && cd trip-scout
python -m venv .venv && .venv/Scripts/activate      # Windows  (macOS/Linux: source .venv/bin/activate)
pip install -r requirements.txt
python run_demo.py                                   # starts the MCP server (:8765) and the Alexa+ simulator (:8770)

Open http://127.0.0.1:8770, press the mic (Chrome/Edge) or type. Try the chips: "I live in New York" → "Plan a long weekend in Barcelona in November under 2500 dollars" → "Watch that price" → "Did any of my prices drop?"

Use the MCP server from any MCP client (Claude Desktop, Cursor, MCP Inspector, Alexa+):

python -m trip_scout.server        # http://127.0.0.1:8765/mcp
npx @modelcontextprotocol/inspector   # connect to the URL above, transport "Streamable HTTP"

Related MCP server: Google Flights MCP Server

Architecture

flowchart LR
  U[User voice] --> A[Alexa+ / simulator host<br/>LLM + Agent Skill]
  A -- MCP 2025-11-25<br/>Streamable HTTP --> S[Trip Scout MCP server]
  S --> P[Planner<br/>parallel fan-out, ranking]
  P --> F[Google Flights engine<br/>tfs protobuf]
  P --> H[Google Hotels engine<br/>ts/qs protobuf, paging]
  S --> DB[(SQLite<br/>profiles, price watches, trips)]
  S -- ui://trip-scout/view.html --> V[MCP Apps view<br/>Echo Show cards]
  V -- tools/call, ui/open-link --> A

Tools

Every tool returns structuredContent (for the screen) plus a voice-ready speech sentence — so the host can answer immediately after the tool call without a second LLM round trip.

Design decisions (and why)

• Voice first. Answers are one or two sentences, dates are spoken ("Friday, November 13"), prices rounded, no IDs or flight numbers read aloud — the card carries the detail.

• Latency. Flight searches take ~1.2 s; a 14-date comparison ~3 s; a complete trip plan ~10–15 s with live progress notifications. The host skips the second LLM call when a tool already produced the sentence.

• Trustworthy picks. A 5.0 hotel with 12 reviews does not beat a 4.7 with 3,000 (Bayesian average); dorms and single rooms are excluded for groups.

• Honest budgets. If nothing fits, Trip Scout says the cheapest total instead of pretending.

• No lock-in. The simulator's LLM is any OpenAI-compatible endpoint — a free public one by default, Amazon Bedrock by setting LLM_BASE_URL / LLM_MODEL. If the LLM is down, a rule-based intent router keeps the demo alive.

• Security. Origin and Host headers are validated (DNS-rebinding protection per the spec), the server binds to localhost by default, TRIP_SCOUT_TOKEN enables bearer auth for deployments, user data is isolated per x-user-id.

Tests

python -m pytest tests/test_units.py     # offline: dates, places, protobuf encoders, persistence, intents
python tests/smoke_mcp.py                # live: real MCP client over Streamable HTTP, every tool
python tests/conversation.py             # live: the full demo conversation through the Alexa+ simulator

Configuration

Built during the hackathon

Everything in this repository was written for the Build, Ship, Shape hackathon (September–October 2026). The flight and hotel engines are also published as standalone open-source libraries: google-flights-python and google-hotels-python.

Product feedback and the friction log for the Amazon teams: docs/FEEDBACK.md.

Disclaimer

Unofficial; not affiliated with Google. Uses publicly available search results at human-like request rates. Prices change constantly — always confirm on the booking site.