It was right on every nit, so it was surprising how well the model knows these things. If I ever release this I'll probably need the SERP API or Google Maps SDK (which I've heard is very expensive now), but for a personal trip where I will verify manually, using the LLM is okay for now.
tools=[{"type": "google_search"}]
Using grounding in Gemini is indeed backed by the same canonical data source for business information (like opening hours) as Google Maps. This stuff is available in its own API for a GCP fee, but we’ve built tooling to connect it to the Gemini agentic ecosystem as well.
The info isn't in the model weights.
Because of where I live, there are three viable airports for any given flight I might want to take, which historically has made shopping a real pain. But Gemini (and only Gemini) has greatly simplified it. Pramble plus date range plus destination and it very quickly generates potential itineraries with costs, total travel time (driving included), etc.