How to Build a Travel Research Agent with Google Flights, Hotels, and Maps

The TL;DR

Travel research agents bring flights, hotels, maps, and itinerary planning into one connected workflow, reducing the need to switch between separate platforms or repeat the same trip details.

• • Manual Planning vs AI Agents

  Traditional trip planning separates flights, accommodation, transport, and maps across multiple tools. AI agents connect those steps through shared context, so changes to one part of the trip can inform the rest of the plan.

• • How Travel Planning Improves

  Destinations, hotel locations, travel times, schedules, and budgets stay connected throughout the planning process. This makes comparisons faster, reduces repeated searches, and helps users build more practical itineraries.

Planning a trip usually means jumping between three tools: one for flights, one for hotels, and one for maps to check whether the options actually work together. Every change to the dates, destination, or hotel means repeating the same comparison across all three.

MCP360 removes that back-and-forth by giving one agent access to Google Flights, Google Hotels, and Google Maps at the same time. The agent can keep your dates, budget, hotel preferences, and location constraints in one context, then compare them as a connected plan instead of separate searches.

This blog shows how to connect those three travel servers in MCP360 and use them through YourGPT’s chat interface. The result is a travel research agent that can compare flights, evaluate hotel locations, check travel times, and update the itinerary without losing context between steps.

Why Travel Research Breaks Down Across Multiple Tools

Take a five-day trip to Lisbon in September. Flight prices live on Google Flights or an airline’s own site. Hotel details sit somewhere else entirely, and confirming a hotel is close to Alfama, the neighborhood you want to stay near, means opening a map as a third step. None of that is unusual. It’s how travel search has worked for years. What makes it slow comes down to a few things.

• Data lives on separate platforms. Flights, hotels, and location data each sit behind their own search, with no shared thread connecting them. Checking one doesn’t tell you anything about the other two.
  
• Decisions need more than one data point. Picking a hotel for the Lisbon trip means weighing its rating against its distance from Alfama, its price against whatever dates the flight calendar shows as cheapest, and its neighborhood against wherever you plan to spend time. No single search returns all three.
  
• One question leads to several more. A flight question turns into an airport transfer question. A hotel pick raises a question about what’s actually walkable nearby. Travel research rarely resolves in one pass.
  
• Changes force a redo. A different week, a different budget, a city added to the route, and the traveler goes back through flights, hotels, and the map again, because none of the earlier research carries forward on its own.

None of these four problems is hard to solve on its own. The trouble is that all four show up on the same trip, every time. That gap is what a travel research agent closes.all show up on the same trip, every time. That’s the gap a travel research agent is built to close.

What Is a Travel Research Agent?

A travel research agent doesn’t book anything. It searches, compares, and carries context forward between questions. Go back to that Lisbon trip. Ask it for flights from Chicago in September, then ask about hotels near Alfama, and it should already know the dates and destination without you repeating them.

That context retention is the entire value proposition. A flight search tool alone is a form with fields. What turns it into an agent is a model that can hold “Lisbon, mid-September, two adults” across a dozen follow-up questions and use that state to narrow every subsequent search.

How the Agent Turns Separate Searches Into One Plan

An agent built on these three tools doesn’t treat the Lisbon research as three separate lookups. Ask it to compare two neighborhoods for a hotel, and it can pull search_hotels results for both, then run search_nearby against each one before answering, weighing the tradeoff instead of returning two lists side by side and leaving the comparison to you.

That matters most when questions build on each other. A change in travel dates should, on its own, tell the agent to check search_flight_calendar and search_hotels again for the new week, not require starting a fresh conversation. A multi-city trip means the agent tracks flight legs and hotel picks together across every stop, so a delay on leg two gets weighed against what it does to leg three, not evaluated on its own.

The result reads less like a search engine and more like someone who remembers what you asked five questions ago. That’s what this build is actually for, and it’s why the three servers below are worth wiring up as a set rather than one at a time.

Create Your Own Travel Research Agent with YourGPT

YourGPT handles the agent layer, covering natural language understanding, conversation context, and response generation. MCP360 connects the agent to Google Flights, Hotels, and Maps through a single MCP endpoint, so the agent can pull live travel data without separate integrations for each tool. You can even read the full integration guide before connecting MCP360 with YourGPT. You have two ways to expose them:

• Individually, one endpoint per server, if you only want the agent touching these three tools:

https://connect.mcp360.ai/v1/google-flights/mcp?token=YOUR_API_KEY
https://connect.mcp360.ai/v1/google-hotels/mcp?token=YOUR_API_KEY
https://connect.mcp360.ai/v1/google-maps/mcp?token=YOUR_API_KEY

• Through the Universal Gateway, one endpoint for your entire MCP360 catalog, which is the better option if this agent will eventually pick up other tools too. Here is how you can create it using Universal gateway endpoint:

Step 1. Create a Travel Research AI Agent

• Sign up for YourGPT and create a new custom AI agent.

• Enter a name for your agent, such as Travel Research Assistant, and configure the welcome message.
• Review the settings and click Create Agent.

Step 2. Configure Your Agent for Travel Research

• Open the agent settings and define the agent’s role and instructions.
• Configure the agent to research destinations, compare travel options, answer travel-related questions, and assist with trip planning.

• Add relevant travel resources, FAQs, and other knowledge sources to improve response quality.

• Save the configuration after reviewing the settings.

Connect MCP360 to Your Travel Research Agent

YourGPT handles the agent layer, covering natural language understanding, conversation context, and response generation. MCP360 connects the agent to Google Flights, Hotels, and Maps through a single MCP endpoint, so the agent can pull live travel data without separate integrations for each tool. You have two ways to expose them:

Step 1. Copy MCP Endpoint

After creating and configuring your travel research agent, the next step is connecting it to MCP360. This gives the agent access to external travel tools through a single MCP connection.

• Log in to MCP360 and open an existing project or create a new one.
• From the sidebar, navigate to MCP Servers.

• Select the tools you want to use with your travel research agent. You can also use the Universal MCP Gateway to access all available tools through a single endpoint.
• Copy the MCP URL provided by MCP360.

Step 2. Configure MCP360 in Your Agent

• Log in to YourGPT and open the dashboard.
• From the sidebar, navigate to Functions → MCP Servers.

• Paste the MCP360 URL into the MCP Server configuration field.

Step 3. Verify MCP Connection

• Click Test Connection to verify that the setup is working correctly.

• After a successful test, click Continue, then select Create Function.

• YourGPT will load all tools available through the MCP360 connection.

Once setup is complete, the travel research agent can access MCP360 tools directly from YourGPT.

Step 4. Research Travel Queries Using Your Agent

Now test the setup by sending a travel query to the agent. The agent will use MCP360 tools in the background to fetch relevant travel information and return results.

Real-World Use Cases for Travel Research Agents

The value of this setup becomes clearest when travel decisions involve more than one moving part. Here are the scenarios where teams and individuals get the most out of a connected travel research agent.

1. Multi-city routing: A trip through three or four cities means checking flight connections, layover cities, and hotel positions in each stop against each other. The agent can run search_flights for each leg and search_nearby for each hotel option without losing track of the earlier legs.
2. Location-based hotel filtering: Star ratings only tell part of the story. Pairing search_hotels with search_nearby lets the agent check whether a highly rated hotel is actually walkable from the neighborhoods the traveler cares about, rather than just sorting by review score.
3. Neighborhood quality checks: A hotel that’s close to the right area on paper can still be a bad pick if the area itself doesn’t hold up. search_nearby combined with get_reviews lets the agent look at what’s actually around a hotel, the cafes, the reviews for nearby spots, not just the hotel’s own rating.
4. Flexible-date cost comparison: search_flight_calendar turns “when should I go” into a real answer instead of a guess. The agent can pair that against hotel pricing for the same date range to spot the cheapest overall week rather than the cheapest single flight.
5. Itinerary sanity checks: A daily plan that looks fine on paper can fall apart once real transit times get involved. search_nearby and get_place_details let the agent flag a schedule that has someone crossing a city twice in one afternoon before that becomes a problem on the actual trip.

None of this needs extra code once the connection from Step 2 is in place. It’s just what the model can already do once it can call all three tools in the same conversation. Before you point this at anything beyond your own testing, there’s one more thing worth setting up first.

Frequently Asked Questions

Conclusion

A travel research agent built on Google Flights, Google Hotels, and Google Maps closes the gap that usually sends a traveler across a dozen tabs. Checking a flight’s arrival time against a hotel’s check-in window, or a hotel’s location against the places someone wants to visit, happens inside one conversation instead of three separate searches, and none of it requires custom integration code.

The same pattern extends past travel. The MCP360 gateway behind these three servers also links a currency converter for multi-country pricing and a weather server for packing and timing decisions, with the rest of the catalog available as trip planning gets more specific. Booking platforms, loyalty programs, and visa and entry requirements are the kinds of MCP servers likely to follow, and an agent built this way already has the plumbing to pick each one up through the same connection, using one API key instead of a fresh integration for each addition.

Start with the query that usually means a browser full of tabs and a spreadsheet, comparing two neighborhoods on price, rating, and walk distance to what you plan to see. Get the three servers here live and answering that question first. From there, browse MCP360’s full server catalog, or see our roundup of essential MCP servers, for what to connect next.