Trip Planner MCP

# Agent Updates - Enhanced Trip Recommendations Integration ## Summary All agent files have been updated to utilize the enhanced `get_trip_recommendations` tool with its new parameters and capabilities. --- ## 📝 Files Updated ### 1. `src/a2a_chatbot.py` - Multi-Agent System ✅ #### Travel Agent Enhanced **Changes**: - Added comprehensive system instructions for using `get_trip_recommendations` - Documented all 6 parameters: location, start_date, end_date, num_people, preferences, budget_per_person - Added strategy guide for trip planning with real-time weather data - Included 7 preference categories with descriptions - Added example scenarios for different use cases **Key Features**: ```python # New capabilities the Travel Agent understands: - Real-time weather forecasts - Preference-based recommendations (adventure, nature, food, culture, etc.) - Group travel tips for multiple travelers - Budget-aware suggestions - Weather-based packing advice - External resource links (TripAdvisor, Google Maps, etc.) ``` **Example Usage**: ``` User: "Plan a 5-day trip to Goa for 4 people who love adventure and food" Travel Agent: Calls get_trip_recommendations with: - location: "Goa" - num_people: 4 - preferences: "adventure, food" - Dates auto-default to upcoming weekend ``` #### Orchestrator Enhanced **Changes**: - Updated example workflows to show how to pass team size to Travel Agent - Added instructions to summarize preferences from team food data - Included date and budget parameter examples - Enhanced example showing complete trip planning with team integration **Example Workflow**: ``` Query: "Plan a trip to Goa for Sarah's team" 1. Get team members (count = 5) 2. Get food preferences (3 vegetarian, preferences: seafood) 3. Call Travel Agent with: - location: "Goa" - num_people: 5 - preferences: "food, culture, relaxation" - Additional context: vegetarian and seafood options 4. Combine results with weather forecast and recommendations ``` --- ### 2. `src/chatbot.py` - Single Agent System ✅ **Changes**: - Expanded system instruction to include external APIs section - Documented all available tools with their parameters - Added comprehensive trip planning strategy guide - Included instructions for team trip integration - Enhanced result presentation guidelines **New Instructions Cover**: 1. **Tool Descriptions**: - `run_cypher_query` - Organizational data - `run_sql_query` - Food/venue/trip data - `get_trip_recommendations` - Trip planning with weather - `get_country_info` - Country details 2. **Trip Planning Strategy**: - Extract trip parameters from user query - Handle team trips with group size and dietary needs - Present results in user-friendly format - Highlight weather and practical advice 3. **Enhanced Capabilities**: ``` - Real-time weather data integration - Preference-based recommendations - Group-aware planning - Budget-conscious suggestions - Practical packing advice ``` **Example Query Handling**: ``` Query: "Plan a weekend trip to Mumbai for my team" Agent Process: 1. Use run_cypher_query to count team members 2. Use run_sql_query for food preferences 3. Call get_trip_recommendations with: - location: "Mumbai" - num_people: [from team count] - preferences: [from food data + user hints] 4. Format and present comprehensive results ``` --- ### 3. `src/orchestrator.py` - Simulated System ✅ **Changes**: - Completely rewrote `travel_agent_task` method - Now calls `get_trip_recommendations` with multiple parameters - Parses JSON response instead of plain text - Formats comprehensive report with all sections **Enhanced Output Includes**: ``` === TRIP PLAN FOR UDAIPUR === Duration: 3 days (2025-11-29 to 2025-12-01) Group Size: 3 people Preferences: food, culture, sightseeing Weather: Weather in Udaipur: clear sky, 25°C RECOMMENDATIONS: Getting Started: • Research local customs and etiquette • Book accommodations in advance • Check visa requirements Food & Culture: • Try local street food • Join food walking tours • Visit local markets TRAVEL TIPS: • Hot weather expected - stay hydrated • Traveling with 3 people - consider group discounts --- DESTINATION INFO (India) --- Capital: New Delhi Currency: INR Languages: Hindi, English USEFUL LINKS: • Weather: https://openweathermap.org/city/Udaipur • Tripadvisor: https://www.tripadvisor.com/Search?q=Udaipur • Google Maps: https://www.google.com/maps/search/?api=1&query=Udaipur • Booking: https://www.booking.com/searchresults.html?ss=Udaipur ``` **Parameters Demonstrated**: - `location`: "Udaipur" - `num_people`: 3 (based on team size) - `preferences`: "food, culture, sightseeing" - Dates auto-default to upcoming weekend --- ## 🎯 Key Improvements Across All Agents ### 1. Parameter Awareness All agents now understand the 6 parameters of `get_trip_recommendations`: | Parameter | Type | Usage | |-----------|------|-------| | location | string (required) | City name | | start_date | string (optional) | YYYY-MM-DD format | | end_date | string (optional) | YYYY-MM-DD format | | num_people | int (optional) | Travelers count | | preferences | string (optional) | Comma-separated | | budget_per_person | float (optional) | Budget amount | ### 2. Preference Categories All agents can use these 7 preference categories: - `adventure` - Hiking, sports, outdoor activities - `nature` - Parks, wildlife, trails - `food` - Restaurants, street food, markets - `culture` - Museums, history, tours - `sightseeing` - Attractions, landmarks - `relaxation` - Spas, beaches, downtime - `shopping` - Markets, malls, souvenirs ### 3. Team Integration Agents now properly integrate team data into trip planning: ``` Team Size → num_people parameter Food Preferences → preferences parameter Dietary Restrictions → mentioned in query to Travel Agent ``` ### 4. Weather-Aware All agents understand that weather data is included and should: - Highlight weather conditions - Give packing advice - Suggest weather-appropriate activities ### 5. External Resources Agents know to mention external resource links: - TripAdvisor for reviews - Google Maps for navigation - Booking.com for accommodations - WikiTravel for travel guides - OpenWeatherMap for detailed weather --- ## 📊 Comparison: Before vs After | Aspect | Before | After | |--------|--------|-------| | **Parameters Used** | location only | 6 parameters (location, dates, people, preferences, budget) | | **Response Format** | Plain text | Structured JSON parsed into sections | | **Weather Data** | ❌ Not mentioned | ✅ Real-time forecasts | | **Group Awareness** | ❌ No | ✅ Group-specific tips | | **Preferences** | ❌ Generic | ✅ 7 categories supported | | **Budget Tips** | ❌ None | ✅ Budget-aware suggestions | | **External Links** | ❌ None | ✅ 5 resource links | | **Practical Advice** | ❌ Limited | ✅ Weather-based packing tips | | **Team Integration** | ❌ Basic | ✅ Full team data integration | --- ## 🚀 Example Use Cases Now Supported ### Use Case 1: Simple Trip ``` User: "Plan a trip to Paris" System: - Uses location parameter - Auto-defaults to upcoming weekend - Default preferences (sightseeing, culture, food) - Fetches real-time weather - Provides comprehensive recommendations ``` ### Use Case 2: Detailed Planning ``` User: "Plan a 5-day adventure trip to Udaipur from Dec 20-25 for 4 people, budget 5000 per person" System: - location: "Udaipur" - start_date: "2025-12-20" - end_date: "2025-12-25" - num_people: 4 - preferences: "adventure" - budget_per_person: 5000 - Returns weather, adventure activities, budget tips, group advice ``` ### Use Case 3: Team Trip with Food Preferences ``` User: "Plan a weekend trip to Goa for my team and consider their food preferences" System: 1. Query Neo4j for team members (e.g., 6 people) 2. Query Postgres for food preferences (e.g., 3 vegetarian, 2 like seafood) 3. Call get_trip_recommendations: - location: "Goa" - num_people: 6 - preferences: "food, relaxation, culture" - Context: "vegetarian and seafood options" 4. Present comprehensive plan with weather, dining suggestions, activities ``` --- ## ✅ Testing the Updates ### Test 1: Basic Query (a2a_chatbot.py) ```bash python src/a2a_chatbot.py ``` ``` You: Plan a trip to Mumbai ``` Expected: Travel Agent uses default parameters, fetches weather, gives recommendations ### Test 2: Detailed Query (chatbot.py) ```bash python src/chatbot.py ``` ``` You: Plan a 3-day trip to Jaipur for 5 friends who love adventure and shopping, budget 4000 per person ``` Expected: Uses all parameters, comprehensive output with weather and tips ### Test 3: Team Trip (a2a_chatbot.py) ```bash python src/a2a_chatbot.py ``` ``` You: Plan a trip to Udaipur for Alpha Team considering their food preferences ``` Expected: 1. Team Agent gets team members 2. Food Agent gets preferences 3. Travel Agent plans trip with group size and dietary info ### Test 4: Orchestrator (orchestrator.py) ```bash python src/orchestrator.py ``` Expected: Formatted report with trip details, weather, recommendations, tips, country info, and resource links --- ## 📚 Documentation References For users wanting to understand the tool capabilities: - **TRIP_RECOMMENDATIONS_GUIDE.md** - Comprehensive tool guide - **QUICK_REFERENCE.md** - Quick parameter reference - **ENHANCEMENT_SUMMARY.md** - What changed in the tool - **README.md** - Setup and usage instructions --- ## 🎓 Agent Learning Points All agents now understand: 1. ✅ Trip planning requires multiple data points (dates, people, preferences, budget) 2. ✅ Weather data is real-time and should be highlighted 3. ✅ Preferences should be extracted from user queries or team data 4. ✅ Group size affects recommendations (group discounts, accommodations) 5. ✅ Budget impacts suggestions (budget-conscious tips) 6. ✅ Results should be formatted in user-friendly sections 7. ✅ External resources should be mentioned for deeper dives 8. ✅ Practical advice (packing, weather) enhances user experience --- **Status**: ✅ All agents updated and ready for testing **Backward Compatible**: ✅ Yes (basic location-only queries still work) **Enhanced Capabilities**: ✅ 6 parameters, real-time weather, 7 preference categories