README.md

metadata

title: TravAI
emoji: 📊
colorFrom: green
colorTo: pink
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: false

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

🗺️ TravAI MCP Tool

Track: mcp-server-track
Hackathon: Agents & MCP Hackathon 2025
Team: Sarthak Bhardwaj, Maria Dirnberger, Pulkit Thukral

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🌟 Overview

TravAI MCP Tool is an agent accessible Gradio app that functions as a Model Context Protocol (MCP) Server. It helps users and by extension, AI agents plan smart travel routes and check localized weather forecasts for their trips.

This app includes:

• 🧭 Google Maps Directions: Get step-by-step, optimized routes between locations, optionally with waypoints, travel mode, and a static map snapshot.
• 🌤️ Weather Forecast: Get detailed weather insights (including hourly breakdowns and UV index) for a given location and date range, using the Open-Meteo API.

Perfect for itinerary building agents or AI assistants that need contextual, real world travel and weather data.

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🚀 Live Demo

👉 Launch the MCP Tool on Hugging Face Spaces
🎥 Watch a quick demo of the tool in action

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🔧 Features

📍 Google Maps Directions Tool

Inputs:

• Source Location (text)
• Destination Location (text)
• Travel Mode (driving / walking / bicycling / transit)
• Departure Time (in YYYY-MM-DD HH:MM format)

Output:

• Markdown summary of route with:
  • Total distance and duration
  • Step by step directions
  • Static map snapshot

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🌦️ Weather Forecast Tool

Inputs:

• Location Name
• Start Date
• End Date

Output:

• Daily weather breakdown (sunrise, sunset, UV index, temperature)
• Hourly details (up to 7 days ahead): temperature, weather condition, UV index, visibility
• Markdown-formatted output

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📜 How to Use (MCP-Compatible)

To use this tool via MCP, simply connect any MCP-compatible client such as:

• Claude Desktop
• Cursor
• Tiny Agents

This app exposes two API endpoints:

• /predict/get direction
• /predict/get weather forecast

Agents can interact with it using structured input fields defined in the Gradio UI and receive Markdown or structured output to reason over.

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💡 Motivation

We wanted to build a travel-assistant-style MCP tool that an LLM agent could query in natural language to:

• Check if walking from Brandenburg Gate to Museum Island makes sense.
• See if it might rain in Paris during the second week of June.
• Create routes with coffee stops between meetings.

Combining live navigation logic with real weather forecasting creates powerful context for itinerary-focused AI agents.

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🛠️ Tech Stack

• Gradio 4.x (with mcp_server=True)
• Google Maps Directions API
• Open-Meteo API
• geopy, pandas, dotenv, requests, re, datetime, json

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🔐 API Keys

• Requires a valid GOOGLE_API_KEY in a .env file for route planning.
• Weather data is free and does not require a key (via Open-Meteo).

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📥 Setup Instructions

1. Clone this repo
2. Create a .env file with your Google API key:

   GOOGLE_API_KEY=your_api_key_here
   

3. Install dependencies

   pip install -r requirements.txt
   

4. Run locally

   python app.py
   

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🎯 Future Enhancements

• Auto-detect location from IP
• Add alerts for UV index or rain warnings
• Suggest alternate routes based on weather
• Integrate with calendar events for smart trip planning

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📬 Contact

Built by Sarthak Bhardwaj, Maria Dirnberger, and Pulkit Thukral