Gemini's New Superpowers: Combining Google Search, Maps, and Your Own Code in a Single API Call

Have you ever tried to build a smart assistant and hit a wall? You know, that frustrating moment when you realize it can either look something up online or run a piece of your custom code, but it can't do both at the same time. It feels clunky, unnatural. Humans multitask. We can look up a restaurant's address while checking our calendar to see if we're free. Why shouldn't our AI?

Well, grab a coffee, because things just got a lot more interesting. Google recently rolled out some game-changing updates to the Gemini API that are all about breaking down those walls. We can now build applications that feel less like rigid command-line tools and more like genuine, multi-talented assistants.

In this post, I'm going to walk you through exactly what this means. We're not just talking theory here. We're going to look at how you can combine built-in tools like Google Search and Google Maps with your very own custom functions, all within a single, elegant API call. Let's dive in and see how to build the kind of AI you've actually been dreaming of.

The Basics: Mixing Google Search with Your Own Tools

Let's start with the headline feature. Imagine you want to ask an AI: "What's the northernmost city in the United States, and what's the weather like there today?"

In the past, this was a tricky, two-step problem. The AI would first need to search the web to find the city (Utqiagvik, Alaska, by the way). Then, you’d have to take that answer and feed it into a separate call to a weather API. It worked, but it was disconnected.

Now, we can do it all in one smooth conversation. We can give Gemini access to Google Search and declare our own custom function, let's call it getWeather, in the same request.

Here’s how it plays out:

1. You ask the question. Gemini sees that it needs two pieces of information.
2. Gemini acts. It uses its built-in Google Search tool to find that the northernmost city is Utqiagvik. At the exact same time, it sees your getWeather function and says, "Hey, I need you to run this for the city 'Utqiagvik, Alaska'." It hands the request back to you.
3. You respond. You run your function (or in a demo, just provide a fake result like "It's very cold") and send it back.
4. Gemini synthesizes. Now, with both the search result and the weather data in hand, Gemini crafts a perfect, natural language answer: "The northernmost city in the U.S. is Utqiagvik, Alaska, where the weather today is very cold, with strong Arctic winds."

See how much smoother that is? It’s a real conversation. The key to unlocking this is a simple setting that tells the API you want it to handle these server-side tool calls automatically. It’s like flipping a switch to turn your simple bot into a resourceful agent.

Juggling Tasks: How Gemini Handles Multiple Requests at Once

Okay, that was cool. But what if your user asks something that requires two of your custom functions at once?

Let's imagine an e-commerce scenario. A customer asks, "I want to buy these headphones. Are they in stock, and how fast can they ship to Beverly Hills?"

This requires checking two different systems: your inventory database and your shipping logistics API. Gemini is smart enough to see this and call both of your custom functions—lookupInventory and getShippingEstimate—in parallel.

But this brings up a critical question: when you get the results back, how does the model know which result goes with which request?

This is where a simple but brilliant mechanism comes into play: unique tool response IDs.

Think of it like a busy restaurant kitchen. When a waiter puts in an order for Table 5 with a steak and a salad, the kitchen gives each item its own ticket number. When the steak is ready, the runner knows to take it to Table 5 because the ticket matches.

Gemini does the same thing. When it requests lookupInventory and getShippingEstimate, it assigns each a unique ID, like a ticket number. When you send back the inventory status and the shipping time, you have to include the matching ID for each. This ensures there's no confusion, and Gemini can confidently say, "Yes, we have 342 in stock, and we can get them to you in 2 days via FedEx."

It's a small detail, but it's absolutely essential for building reliable apps that can handle complex, multi-part user requests without getting their wires crossed.

Making Your App Location-Aware with Google Maps

This next one is just plain fun. You can now "ground" Gemini in the real world using Google Maps. This means you can build apps that are deeply location-aware, giving users hyper-relevant, real-time information about the world around them.

And the best part? For many use cases, it's available on the free tier.

Let's say you're building a travel app. You can pass your user's current latitude and longitude to the API and ask:

• "What are the best Italian restaurants within a 15-minute walk from here?" Gemini won't just give you a generic list. It will tap directly into Google Maps data to find actual places, with real ratings, that are physically close to you.
• "Is there a cafe near this corner that has outdoor seating?" It can answer detailed, specific questions about places by referencing Maps data.
• "Plan a day for me in San Francisco." It can generate a full itinerary, complete with suggestions for the Golden Gate Bridge, a museum, and a nice dinner spot.

What's really powerful is that Gemini doesn't just pull this information out of thin air. It provides its sources! For each place it recommends, you get the name, a link to it on Google Maps, and other metadata.

Even better, for things like trip planning, the API can return a special "widget token." You can embed this token directly into your web or mobile app to display an interactive map of all the recommended locations. It’s a seamless way to take the AI's response and make it visually useful for your user.

Putting It All Together: Building a Simple AI Agent

Now we can start combining these building blocks to create a true AI agent. An agent doesn't just answer questions; it takes action.

Let's go back to our travel app idea. A user, currently in Austin, Texas, says: "Find me a highly-rated BBQ restaurant nearby that's open tonight, and book a table for 4 at 7:30 PM."

This is where the magic of "context circulation" comes in.

1. Gemini uses Google Search to find highly-rated, nearby BBQ joints that are open. It reads the search results and identifies a great candidate, say, "Franklin Barbecue."
2. It uses the context from that search to inform its next action. It sees your custom bookRestaurant function.
3. It calls your function with the arguments it just discovered: restaurant_name="Franklin Barbecue", party_size=4, time="19:30".
4. You "confirm" the booking and send a success message back.
5. Gemini delivers the final summary to the user: "You're all set! I've booked you a table for 4 at Franklin Barbecue for 7:30 PM tonight."

This is a world away from a simple chatbot. The AI is performing a multi-step task, using the output of one tool as the input for the next. It's reasoning, planning, and executing.

The Grand Finale: Chaining Three Tools for Complex Problem-Solving

Ready to see how deep the rabbit hole goes? We can chain even more tools together for some seriously complex problem-solving.

Consider this prompt: "Search for the current US national debt, then calculate the per-capita debt assuming a population of 335 million. Finally, save the result."

This requires a three-tool chain:

1. Google Search: To find the current national debt figure (a massive, ever-changing number).
2. Code Execution: A built-in tool that lets Gemini run a snippet of Python code to perform the calculation (debt / population).
3. A Custom Function: Your own saveAnalysisResult function to store the final number in a database.

Gemini handles this sequence flawlessly. It searches, gets the number, writes and executes the code to do the math, and then calls your save function with the final computed value. The context—the information—flows seamlessly from one step to the next, all within a single turn of conversation. This is the foundation for building AI that can not only find information but also analyze it and act on it.

So, What Does This All Mean for You?

If you've been building with AI for a while, you can probably feel the shift. We're moving beyond simple Q&A bots and into an era of true agentic workflows. These new features in the Gemini API are the building blocks for creating AI assistants that can genuinely do things for us.

They can research, calculate, plan, book, and save—all by intelligently chaining together different tools, both built-in and custom.

The key takeaways are surprisingly simple:

• You can now mix and match built-in tools (like Search and Maps) with your own code in one go.
• The model can handle multiple requests at once and keep track of everything with unique IDs.
• And most importantly, it can use the result from one step to intelligently inform the next, creating a chain of thought that mimics how we solve problems.

The next time you're sketching out an idea for an AI application, don't limit yourself to one-shot answers. Think bigger. What multi-step problems could your users solve if they had an assistant that could search, calculate, and connect to your services all at once? With these tools, you can finally start building it.