Stadia Maps MCP for AI. Solve complex logistics and mapping math in one query.

Stadia Maps gives your agent solid geospatial intelligence for everything from quick street lookups to complex logistics planning. You'll get precise data, no matter how messy the input is.

To nail down a location, you don't just guess; you confirm it first. If you start with a simple street address string, forward_geocode converts that into definite latitude and longitude coordinates. Before you even hit send on an address query, autocomplete_location suggests possible addresses, helping you narrow down the input options so your final results are accurate.

Conversely, if you only have raw latitude/longitude points, reverse_geocode turns 'em back into a readable, structured physical address string.

When it comes to figuring out how people get around, these tools handle everything from simple travel estimates to optimizing entire multi-stop routes. You can run calculate_route between several coordinates and specify if the journey is by car, bike, or foot. For calculating distances and estimated times across a large set of distinct points—say, ten different distribution centers—you use calculate_distance_matrix, which computes all travel times and distances in one go.

But if you're planning an entire trip with multiple stops, don't just connect 'em; you need the best sequence. That’s where optimized_trip_route steps in, figuring out the absolute best order of stops to minimize your total travel time or distance.

For strategic planning, you can use calculate_isochrone to map out a precise geographic area boundary; it shows exactly what's reachable from a central point based on a specific maximum driving time or distance. You also need to clean up messy data: if your GPS tracker sends noisy raw track points, run those through execute_map_matching, and it snaps the coordinates right onto the nearest official road network segment, making them trustworthy for logistics.

For deep analysis, you can pull detailed elevation metrics for any given path or route using get_path_elevation. You can also determine the local time zone associated with specific latitude/longitude points by running get_timezone.

Here's the rundown of what happens:

• Geocoding: The client converts a readable street address string into precise lat/long pairs using forward_geocode. It can also take raw coordinates and return the closest corresponding physical street address via reverse_geocode. To help users input data, autocomplete_location narrows down potential addresses as you type.
• Routing & Optimization: You calculate paths between multiple points using calculate_route, specifying travel modes like car or bike. For complex logistics involving many stops, optimized_trip_route figures out the most efficient sequence to connect those locations and minimize overall distance or time. To get a full picture of distances across many potential start/end spots, calculate_distance_matrix computes all travel times and distances at once.
• Area Modeling & Data Integrity: The agent determines an exact reachable area boundary (an isochrone) using calculate_isochrone, based on a time or distance limit. For dirty GPS data, execute_map_matching corrects raw tracks by snapping points to the official road network. Detailed elevation metrics for any path are retrieved with get_path_elevation. Finally, get_timezone determines the local time zone from specific coordinates.