get_nearby_duty_pharmacies
Find the ten nearest pharmacies open today based on a given address, sorted by proximity for quick and reliable access to medication.
Instructions
Get ten closest pharmacies on duty today, sorted by distance to the given address.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
Implementation Reference
- src/mcp_duty_pharma/duty_pharma.py:25-95 (handler)The main handler function implementing the 'get_nearby_duty_pharmacies' tool. It is decorated with @mcp.tool() for automatic registration with the FastMCP server. Fetches pharmacy data from API, filters for duty today, sorts by distance from given address, and returns top 10.
@mcp.tool() async def get_nearby_duty_pharmacies(address: str) -> list[dict]: """Get nearby pharmacies on duty today. - sorted by distance to the given address. - only ten closest pharmacies are returned. - only pharmacies on duty today are returned. """ headers = {"User-Agent": "MCP Duty Pharma", "Accept": "application/json"} async with httpx.AsyncClient() as client: try: response = await client.get( "https://midas.minsal.cl/farmacia_v2/WS/getLocalesTurnos.php", headers=headers, timeout=30.0, follow_redirects=True, ) response.raise_for_status() all_pharmacies: list[dict] = response.json() except httpx.HTTPStatusError: return [] # Filter pharmacies that are on duty today now = datetime.now(tz=pytz.timezone("America/Santiago")) fecha_hoy = now.isoformat()[0:10] fecha_ayer = (now - timedelta(hours=12)).isoformat()[0:10] all_pharmacies = list( filter( lambda f: f["fecha"] == fecha_hoy or f["fecha"] == fecha_ayer, all_pharmacies, ), ) # Sort pharmacies by distance to the given address location = geocode(address) valid_pharmacies = [] for pharmacy in all_pharmacies: try: lat = float(pharmacy["local_lat"].strip(",")) lng = float(pharmacy["local_lng"].strip(",")) valid_pharmacies.append(pharmacy) logging.debug(f"Processed lat/lng: {lat}, {lng}") except ValueError as e: logging.error( f"Error processing lat/lng for pharmacy {pharmacy}: {e}" ) valid_pharmacies.sort( key=lambda pto: distance.distance( ( float(pto["local_lat"].strip(",")), float(pto["local_lng"].strip(",")), ), (location.latitude, location.longitude), ).km, ) # Return the ten closest pharmacies return [ { "name": pharmacy["local_nombre"], "address": pharmacy["local_direccion"], "phone": pharmacy["local_telefono"], "schedule": pharmacy["funcionamiento_hora_apertura"] + " - " + pharmacy["funcionamiento_hora_cierre"], "zone": pharmacy["localidad_nombre"], } for pharmacy in valid_pharmacies[:10] ]