Trip Planner MCP

# Trip Planner MCP PoC This is a Proof of Concept for a Trip Planner application using the Model Context Protocol (MCP), Postgres, and Neo4j. It demonstrates how an Orchestrator Agent can coordinate with specialized agents (Team, Food, Travel) to answer complex queries. ## Prerequisites 1. **Docker Desktop**: Ensure Docker is installed and running. 2. **Python 3.10+**: Ensure Python is installed. ## Setup 1. **Start the Databases**: Open a terminal in this folder and run: ```bash docker-compose up -d ``` This will start Postgres (port 5432) and Neo4j (ports 7474, 7687). 2. **Create Virtual Environment**: ```bash python -m venv venv # Windows venv\Scripts\activate # Mac/Linux # source venv/bin/activate ``` 3. **Install Dependencies**: ```bash pip install -r requirements.txt ``` 4. **Configure Environment Variables**: Copy `.env.example` to `.env` and add your API keys: ```bash # Windows copy .env.example .env # Mac/Linux # cp .env.example .env ``` Edit `.env` and add: - `GOOGLE_API_KEY`: Get from https://makersuite.google.com/app/apikey - `OPENWEATHER_API_KEY`: Get from https://openweathermap.org/api (free tier) 5. **Seed Neo4j Data**: The Postgres data is seeded automatically. For Neo4j, run this script once: ```bash python src/db_utils.py ``` ## Running the Application ### Option 1: Test Trip Recommendations Tool Test the new trip recommendations tool with real-time weather data: ```bash python test_trip_recommendations.py ``` ### Option 2: Orchestrator (Simulated) Run the hardcoded orchestrator to see the agent workflow without an API key: ```bash python src/orchestrator.py ``` ### Option 3: Chatbot (Single Agent) Run the interactive chatbot (requires Google API Key): 1. Create a `.env` file in the root folder: ``` GOOGLE_API_KEY=your-gemini-api-key OPENWEATHER_API_KEY=your-openweather-api-key ``` Get free OpenWeather API key from: https://openweathermap.org/api (1000 calls/day free) 2. Run the chatbot: ```bash python src/chatbot.py ``` ### Option 4: A2A Chatbot (Multi-Agent System) ✨ **RECOMMENDED** Run the advanced multi-agent system where an Orchestrator coordinates with specialized Team, Food, and Travel agents: 1. Ensure `.env` has your `GOOGLE_API_KEY` and `OPENWEATHER_API_KEY`. 2. Run the A2A chatbot: ```bash python src/a2a_chatbot.py ``` **Features:** - 🤖 **Intelligent Agent Orchestration**: Automatically routes queries to specialized agents (Team, Food, Travel) - 💬 **Conversational Context**: Maintains chat history across sessions (`chat_history.json`) - 🔄 **Parallel Processing**: Multiple agents work together to provide comprehensive responses - 🎯 **Smart Query Understanding**: Uses Gemini AI to analyze and decompose complex travel planning requests - 🌐 **Full MCP Integration**: Seamless access to Neo4j, Postgres, Weather API, and Country Info API The A2A chatbot is **fully functional** and provides the most advanced trip planning experience with multi-agent collaboration. ## How it Works ### Core Components 1. **`src/mcp_server.py`**: The MCP Server exposing multiple tools: * `run_cypher_query`: Execute raw Cypher queries against Neo4j (employee org graph). * `run_sql_query`: Execute raw SQL queries against Postgres (food preferences, trip data). * `get_trip_recommendations`: Get comprehensive travel recommendations with real-time weather data, customizable preferences, dates, group size, and budget. * `get_country_info`: Get country information (currency, languages) from RestCountries API. 2. **`src/orchestrator.py`**: Basic orchestrator with simulated agent workflow (no API key needed). 3. **`src/a2a_chatbot.py`**: **Advanced Multi-Agent System** ✨ * **Orchestrator Agent**: Analyzes user queries and coordinates specialized agents * **Team Agent**: Handles employee data queries using Neo4j graph database * **Food Agent**: Manages food preferences and dietary requirements from Postgres * **Travel Agent**: Provides comprehensive trip planning with weather and location data * **Intelligent Routing**: Uses Gemini AI to determine which agents are needed for each query * **Context Aware**: Maintains conversation history and builds on previous interactions * **Parallel Execution**: Multiple agents can work simultaneously for complex queries 4. **`src/chatbot.py`**: Single-agent chatbot for basic interactions. ## Project Structure * `docker-compose.yml`: Database configuration. * `data/`: Initialization scripts for databases. * `src/`: Source code. * `mcp_server.py`: The MCP tool provider with database query tools. * `orchestrator.py`: Basic orchestrator with simulated workflow. * `db_utils.py`: Database connection helpers. * **`a2a_chatbot.py`**: **Advanced multi-agent chatbot system** ✨ (Fully functional) * `chatbot.py`: Single-agent chatbot for basic use. * `test_trip_recommendations.py`: Test script for trip recommendations tool. * `TRIP_RECOMMENDATIONS_GUIDE.md`: Comprehensive guide for using trip recommendations. * `ARCHITECTURE.md`: Detailed system architecture documentation. * `AGENT_UPDATES.md`: Agent implementation updates and changes. * `.env.example`: Template for environment variables. ## New Features ### 🌦️ Real-Time Weather Integration The `get_trip_recommendations` tool now fetches live weather data from OpenWeatherMap API: - Current conditions and 5-day forecast - Temperature, humidity, wind speed - Weather-based packing tips - Automatic location geocoding ### 🎯 Smart Trip Planning - **Customizable Preferences**: adventure, food, culture, relaxation, shopping, nature - **Group Travel Support**: Tips for traveling with multiple people - **Budget-Aware**: Recommendations based on budget constraints - **Date Flexibility**: Auto-defaults to upcoming weekend if dates not specified - **External Resources**: Links to TripAdvisor, Google Maps, Booking.com, WikiTravel ### 📊 Example Queries You Can Try (A2A Chatbot) 1. **Basic Trip**: "Plan a weekend trip to Paris" 2. **Detailed Planning**: "Plan a 5-day adventure trip to Udaipur from 2025-12-20 to 2025-12-25 for 4 people with preferences: adventure, nature, food. Budget is 5000 INR per person" 3. **Group Trip**: "We are 8 friends planning a relaxation and shopping trip to Bali" 4. **Team Outing**: "Get my team members and plan a 3-day team outing to Goa considering everyone's food preferences" 5. **Multi-Agent Query**: "Who are the employees under Bob Chen and what are their food preferences? Plan a team dinner in San Francisco" 6. **Complex Planning**: "I want to organize a corporate retreat to Jaipur for 15 people next month with cultural activities" ### 🔧 Advanced Database Queries - **`run_cypher_query`**: Execute raw Cypher queries for complex Neo4j graph traversals - **`run_sql_query`**: Execute raw SQL queries for flexible Postgres data retrieval - Full schema documentation in tool descriptions For detailed usage instructions, see `TRIP_RECOMMENDATIONS_GUIDE.md`.