local-places

name	local-places
description	Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
homepage	https://github.com/Hyaxia/local_places
metadata	{"CARNELIAN":{"emoji":"📍","requires":{"bins":["uv"],"env":["GOOGLE_PLACES_API_KEY"]},"primaryEnv":"GOOGLE_PLACES_API_KEY"},"carnelian":{"runtime":"python","version":"0.1.0","sandbox":{"network":"full","resourceLimits":{"maxMemoryMB":512,"maxCpuPercent":50,"timeoutSecs":300},"mounts":[{"host":".","container":"/skill","readonly":true}],"env":{"GOOGLE_PLACES_API_KEY":"${GOOGLE_PLACES_API_KEY}"}},"capabilities":["net.http","process.spawn"]}}

Local Places

Find places, Go fast

Search for nearby places using a local Google Places API proxy. Two-step flow: resolve location first, then search.

Setup

cd {baseDir}
echo "GOOGLE_PLACES_API_KEY=your-key" > .env
uv venv && uv pip install -e ".[dev]"
uv run --env-file .env uvicorn local_places.main:app --host 127.0.0.1 --port 8000

Requires GOOGLE_PLACES_API_KEY in .env or environment.

Quick Start

1. Check server: curl http://127.0.0.1:8000/ping

2. Resolve location:

curl -X POST http://127.0.0.1:8000/locations/resolve \
  -H "Content-Type: application/json" \
  -d '{"location_text": "Soho, London", "limit": 5}'

3. Search places:

curl -X POST http://127.0.0.1:8000/places/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "coffee shop",
    "location_bias": {"lat": 51.5137, "lng": -0.1366, "radius_m": 1000},
    "filters": {"open_now": true, "min_rating": 4.0},
    "limit": 10
  }'

4. Get details:

curl http://127.0.0.1:8000/places/{place_id}

Conversation Flow

1. If user says "near me" or gives vague location, resolve it first
2. If multiple results, show numbered list, ask user to pick
3. Ask for preferences: type, open now, rating, price level
4. Search with location_bias from chosen location
5. Present results with name, rating, address, open status
6. Offer to fetch details or refine search

Filter Constraints

• filters.types: exactly ONE type (e.g., "restaurant", "cafe", "gym")
• filters.price_levels: integers 0-4 (0=free, 4=very expensive)
• filters.min_rating: 0-5 in 0.5 increments
• filters.open_now: boolean
• limit: 1-20 for search, 1-10 for resolve
• location_bias.radius_m: must be > 0