suggest_meeting_point

Instructions

Implementation Reference

• src/osm_mcp_server/server.py:527-613 (handler)

  The core handler function decorated with @mcp.tool() that implements the suggest_meeting_point tool logic. It calculates the geometric center (average) of multiple input locations and searches for nearby venues of the specified type using the OSMClient's get_nearby_pois method, first in a 500m radius and expanding to 1000m if none found. Returns center point and top 5 matching venues.

  async def suggest_meeting_point(
      locations: List[Dict[str, float]],
      ctx: Context,
      venue_type: str = "cafe"
  ) -> Dict[str, Any]:
      """
      Find the optimal meeting place for multiple people coming from different locations.
      
      This tool calculates a central meeting point based on the locations of multiple individuals,
      then recommends suitable venues near that central point. Ideal for planning social gatherings,
      business meetings, or any situation where multiple people need to converge from different
      starting points.
      
      Args:
          locations: List of dictionaries, each containing the latitude and longitude of a person's location
                    Example: [{"latitude": 37.7749, "longitude": -122.4194}, {"latitude": 37.3352, "longitude": -121.8811}]
          venue_type: Type of venue to suggest as a meeting point. Options include:
                     "cafe", "restaurant", "bar", "library", "park", etc.
          
      Returns:
          Meeting point recommendations including:
          - Calculated center point coordinates
          - List of suggested venues with names and details
          - Total number of matching venues in the area
      """
      osm_client = ctx.request_context.lifespan_context.osm_client
      
      if len(locations) < 2:
          raise ValueError("Need at least two locations to suggest a meeting point")
      
      # Calculate the center point (simple average)
      avg_lat = sum(loc.get("latitude", 0) for loc in locations) / len(locations)
      avg_lon = sum(loc.get("longitude", 0) for loc in locations) / len(locations)
      
      ctx.info(f"Calculating center point for {len(locations)} locations: ({avg_lat}, {avg_lon})")
      
      # Search for venues around this center point
      venues = await osm_client.get_nearby_pois(
          avg_lat, avg_lon, 
          radius=500,  # Search within 500m of center
          categories=["amenity"]
      )
      
      # Filter venues by type
      matching_venues = []
      for venue in venues:
          tags = venue.get("tags", {})
          if tags.get("amenity") == venue_type:
              matching_venues.append({
                  "id": venue.get("id"),
                  "name": tags.get("name", "Unnamed Venue"),
                  "latitude": venue.get("lat"),
                  "longitude": venue.get("lon"),
                  "tags": tags
              })
      
      # If no venues found, expand search
      if not matching_venues:
          ctx.info(f"No {venue_type} found within 500m, expanding search to 1000m")
          venues = await osm_client.get_nearby_pois(
              avg_lat, avg_lon, 
              radius=1000,
              categories=["amenity"]
          )
          
          for venue in venues:
              tags = venue.get("tags", {})
              if tags.get("amenity") == venue_type:
                  matching_venues.append({
                      "id": venue.get("id"),
                      "name": tags.get("name", "Unnamed Venue"),
                      "latitude": venue.get("lat"),
                      "longitude": venue.get("lon"),
                      "tags": tags
                  })
      
      # Return the result
      return {
          "center_point": {
              "latitude": avg_lat,
              "longitude": avg_lon
          },
          "suggested_venues": matching_venues[:5],  # Top 5 venues
          "venue_type": venue_type,
          "total_options": len(matching_venues)
      }