It takes 15 minutes for the Caltrain to get from Sunnyvale to SAP Center

That’s how long it took our MCP server to go from “how many bus stops are in Maryland” to an answer

I’ve been doing a lot of reading lately on how AI is going to transform spatial workloads and that curiosity led me to this post on geoMusings. Here, Bill is demonstrating how Claude Code and agent skills capabilities can be used to wire up a chat-to-query-results interface in a few hours. He showcased the new skill by getting the agent to query his local Postgres instance for the number of Metro bus stops in Maryland, which returned a precise 4,563.

I need to count the number of records in the metro_bus_stops table that are inside Maryland.The database is at localhost:5432, database name is “dev”,user “postgres” with password “postgres”. Points table: public.metro_bus_stops (geometry column: geom, id column: id)Polygons table: public.maryland_boundary (geometry column: geom, name column: name)

As a dabbler of AI agents and a minor contributor to Wherobots’ very own MCP server, I immediately wondered how our MCP server would do against such a challenge. So I fired up my VS Code and just straight up asked:

“How many bus stops are in Maryland?”

Bear in mind, at the time I did not know if we have any data with bus stops in it in Wherobots’ data catalogs, I did not know what shape that data was in, I did not know if the MCP server could come up with a reasonable administrative boundary for Maryland, etc. And I fired off this query just as my CalTrain was departing Sunnyvale station.

In about 5 minutes, the MCP server already identified two tables with bus stop information called places_place under the Overture Maps Foundation database in Wherobots Open Catalog. It achieved that by exploring our catalog and running sample queries against those tables to find the right data; all with zero human intervention. We are right about Lawrence Station at the point.

In the next 5 minutes, the MCP server ran a series of queries against that table, self-identified errors (i.e., got 0 results and understood it was not expected), adjusted the query, switched tables, changed approaches until it was able to produce actual results. Our MCP server believes there are 19,740 bus stops in Maryland which is ~5 times as many as Bill’s post suggests. We just got to Santa Clara station, by the way, for those of you who are still following.

So being a good aspiring data engineer, I challenged the MCP server:

Why does this blog think there are only 4563 then? https://blog.geomusings.com/2026/01/14/spatial-analysis-with-claude-code/

The MCP server went back to work and gave me the diagnosis; Bill’s query is focused on Metro bus stops and my original question did not specify that:

So in the last 5 minutes of this journey, I asked it to focus on Washington Metropolitan Area Transit Authority (WMATA) bus stops only and see what it comes up with! And just as we were about to pull into San Jose Diridon Station, the MCP server told me that there are 6,224 Metro bus stops in Maryland. 

Now, whether there are 4,563 Metro bus stops in Maryland or 6,224 ones, is a matter that shall be validated with people far more knowledgeable than myself on buses and their stops. The main point is that AI is making it possible for non-experts like myself to go from a question (expressed in natural language) to real insights in minutes (well a 15-minute train ride to be precise). Wherobots MCP is giving the AI the ability to answer questions about the real-world. 

In the real world, I would have asked the MCP server to generate a Notebook for me to reproduce this output and plot it on a map. I would then share that with my colleague to help me validate, correct and optimize my findings. What would have taken days to weeks (to go from theory to some early explorations to a shareable PoC and, finally, to production-quality code) can now be achieved in a matter of hours. 

The Caltrain experiment was just one question. In our recent office hours, we walked through the MCP server end to end, showing how it explores catalogs, generates spatial queries, debugs errors, and produces reproducible outputs. See the full workflow in action.

Want to get started with our MCP server? Check out our getting started guide. It takes less than 5 minutes to configure the server and start chatting with the physical world! 

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