local-places

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Search for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.

⭐3.1kstars🔀474forks📁View on GitHub🕐Updated Jan 11, 2026
[Web Research]

When & Why to Use This Skill

The Local Places skill enables Claude to perform real-time searches for physical locations, businesses, and points of interest using a local Google Places API proxy. It bridges the gap between the AI and the physical world by allowing users to resolve geographic locations, filter results by ratings or price levels, and retrieve detailed venue information directly within the chat interface.

Use Cases

  • Local Business Discovery: Finding highly-rated restaurants, cafes, or gyms in a specific neighborhood that are currently open.
  • Travel and Itinerary Planning: Identifying points of interest and essential services when planning trips to new cities or unfamiliar areas.
  • Market Research and Lead Generation: Compiling lists of specific business types within a defined radius for competitive analysis or outreach.
  • Real-time Navigation Support: Quickly fetching addresses, ratings, and price levels for venues to help with immediate decision-making.
namelocal-places
descriptionSearch for places (restaurants, cafes, etc.) via Google Places API proxy on localhost.
homepagehttps://github.com/Hyaxia/local_places
metadata{"clawdbot":{"emoji":"📍","requires":{"bins":["uv"],"env":["GOOGLE_PLACES_API_KEY"]},"primaryEnv":"GOOGLE_PLACES_API_KEY"}}

📍 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}'
  1. 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
  }'
  1. 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

Response Format

{
  "results": [
    {
      "place_id": "ChIJ...",
      "name": "Coffee Shop",
      "address": "123 Main St",
      "location": {"lat": 51.5, "lng": -0.1},
      "rating": 4.6,
      "price_level": 2,
      "types": ["cafe", "food"],
      "open_now": true
    }
  ],
  "next_page_token": "..." 
}

Use next_page_token as page_token in next request for more results.