> ## Documentation Index > Fetch the complete documentation index at: https://docs.dappier.com/llms.txt > Use this file to discover all available pages before exploring further. # 🧲 Dynamic Travel Planner with OpenAI Agents + Real-Time Insights via Dappier MCP This cookbook demonstrates how to set up and leverage **OpenAI Agents** combined with **Dappier MCP** for dynamic travel planning. By integrating real-time data and automated tool orchestration via the **Model Context Protocol (MCP)**, this notebook walks you through a practical approach to building adaptive travel agents. In this cookbook, you'll explore: * **OpenAI Agents SDK**: A powerful toolkit that enables large language models to operate as autonomous agents, use tools, and execute multi-step workflows with memory and structured decision-making. * **Dappier MCP**: A Model Context Protocol server that connects your agents to real-time, rights-cleared, AI-powered tools such as live search, weather, stock data, and content recommendations. * **Dynamic Travel Planning**: A real-world use case where the assistant creates a multi-day itinerary using live weather, events, and hotel data sourced via MCP. This example demonstrates a flexible architecture for building real-time, tool-augmented assistants and lays the foundation for other real-world applications powered by dynamic context, tool use, and AI reasoning. {/* ## 📺 Video Walkthrough Prefer watching? Here’s a video version of this notebook: */} ## 📦 Installation To get started, install the required tools and dependencies: **Step 1: Install `uv` (required to run the Dappier MCP server)** **macOS / Linux**: ```bash theme={null} curl -LsSf https://astral.sh/uv/install.sh | sh ``` **Windows**: ```bash theme={null} powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` *** **Step 2: Install Python Packages** Install OpenAI Agents SDK. ```bash theme={null} pip install openai-agents ``` ## 🔑 Setting Up API Keys You’ll need API keys for both **Dappier** and **OpenAI** to authenticate your requests and access tools. **Dappier API Key** Head to [Dappier](https://platform.dappier.com/profile/api-keys) to sign up and generate your API key. Dappier offers free credits to get started. You can set your API key as an environment variable in your terminal: ```bash theme={null} export DAPPIER_API_KEY=your-dappier-api-key ``` Or programmatically in your Python script: ```python Python theme={null} import os os.environ["DAPPIER_API_KEY"] = "your-dappier-api-key" ``` *** **OpenAI API Key** Visit [OpenAI](https://platform.openai.com/account/api-keys) to retrieve your API key. Set it in your terminal: ```bash theme={null} export OPENAI_API_KEY=your-openai-api-key ``` Or in your Python script: ```python Python theme={null} os.environ["OPENAI_API_KEY"] = "your-openai-api-key" ``` ## ⚙️ Import Dependencies Start by importing all required modules to build the travel planner agent. This includes components from the OpenAI Agents SDK and the Dappier MCP server. ```python Python theme={null} import asyncio import shutil import os from datetime import datetime from agents import Agent, Runner, trace from agents.mcp import MCPServer, MCPServerStdio from openai.types.responses import ResponseTextDeltaEvent ``` These imports enable: * Running the MCP server locally via `MCPServerStdio` * Tracing and managing the agent's execution with `Runner` and `trace` * Streaming the assistant's output using `ResponseTextDeltaEvent` ## 📝 Define User Input We’ll collect basic trip preferences from the user: city, number of days, and travel start date. ```python Python theme={null} def get_user_input(): city = input("Enter the city for your travel: ").strip() num_days = input("Enter the number of days for the trip: ").strip() travel_date = input("Enter the start date of travel (YYYY-MM-DD): ").strip() return city, num_days, travel_date ``` ## 🛰️ Run the Agent with Dappier MCP This function sets up the agent, formulates the user query, and streams the response using tools served via Dappier MCP. ```python Python theme={null} async def run(mcp_server: MCPServer, city: str, num_days: str, travel_date: str): agent = Agent( name="TravelPlanner", instructions=""" You are a dynamic travel planning assistant. Your goal is to build a customized travel itinerary. Use Dappier's tools to get any real time information. """, mcp_servers=[mcp_server], model="gpt-4o-mini" ) query = f""" Generate a {num_days}-day travel itinerary for {city}, tailored to the real-time weather forecast for the selected date: {travel_date}. Follow these steps: Determine Current Date and Travel Period: Use Dappier's real-time search to identify the current date and calculate the trip duration based on user input. Fetch Weather Data: Retrieve the weather forecast for {city} during the selected dates to understand the conditions for each day. Fetch Live Events Data: Use Dappier's real-time search to find live events happening in {city} during the trip dates. Fetch Hotel Deals Data: Use Dappier's real-time search to find the best hotel deals with booking links in {city} during the trip dates. Design the Itinerary: Use the weather insights, live events, hotel deals to plan activities and destinations that suit the expected conditions. For each suggested location: Output: Present a detailed {num_days}-day itinerary, including timing, activities, booking links, weather information for each day and travel considerations. Ensure the plan is optimized for convenience and enjoyment. """ print("\n" + "-" * 40) print(f"Running itinerary generation for {city} starting {travel_date} for {num_days} days") print("\n=== Streaming Start ===\n") result = Runner.run_streamed(agent, query) async for event in result.stream_events(): if event.type == "raw_response_event" and isinstance(event.data, ResponseTextDeltaEvent): print(event.data.delta, end="", flush=True) print("\n\n=== Streaming Complete ===\n") ``` ## 🚦 Initialize and Launch the Workflow The `main()` function sets up the Dappier MCP server, enables tracing for observability, and runs the travel planning agent. ```python Python theme={null} async def main(): city, num_days, travel_date = get_user_input() async with MCPServerStdio( cache_tools_list=True, params={ "command": "uvx", "args": ["dappier-mcp"], "env": {"DAPPIER_API_KEY": os.environ["DAPPIER_API_KEY"]}, }, ) as server: with trace(workflow_name="Dynamic Travel Planner with Dappier MCP"): await run(server, city, num_days, travel_date) ``` ## 🧪 Run the Travel Planner Agent This block checks for the required `uvx` binary and launches the full async workflow. Make sure `uvx` is installed and available in your system path. ```python Python theme={null} if __name__ == "__main__": if not shutil.which("uvx"): raise RuntimeError( "uvx is not installed. Please install it with `pip install uvx` or " "`curl -LsSf https://astral.sh/uv/install.sh | sh`." ) asyncio.run(main()) ``` ```json theme={null} Enter the city for your travel: Tokyo Enter the number of days for the trip: 2 Enter the start date of travel: tomorrow ---------------------------------------- Running itinerary generation for Tokyo starting tomorrow for 2 days === Streaming Start === Here's a cozy and adventurous 2-day itinerary for your trip to Tokyo, tailored for April 10-11, 2025, with considerations for the weather, live events, and accommodation options. --- ### **Day 1: April 10, 2025 (Overcast & Showers)** **Morning:** - **Breakfast at a local café.** - Recommended: **Doutor Coffee Shop** for a hearty breakfast. - **Activity:** Visit **Ueno Park**. - A great place to enjoy flowering cherry blossoms and explore museums. - **Tip:** Bring an umbrella! Expect passing showers throughout the day. **Time: 9:00 AM – 12:00 PM** --- **Lunch:** - **Location:** **Ameyoko Market** - Explore various street foods. - Try some grilled fish skewers and taiyaki for dessert! **Time: 12:30 PM – 1:30 PM** --- **Afternoon:** - **Activity:** Visit the **National Museum of Nature and Science.** - Indoor activity, perfect for the rainy weather. - Engage with interactive exhibits about Japan’s flora, fauna, and technology. **Time: 2:00 PM – 4:30 PM** --- **Evening:** - **Dinner:** Enjoy dinner at **Ippudo Ramen** in Akihabara. - Experience delicious tonkotsu ramen, perfect for a rainy evening. **Time: 5:00 PM – 7:00 PM** --- **Live Event:** - **Explore local venues for music or comedy shows.** - While specific events aren’t listed for April 10, many venues often have surprise performances! --- **Accommodation:** - **Hotel Suggestion:** **HOTEL GROOVE SHINJUKU, A PARKROYAL Hotel** - Price: Approx. **$109/night** - Location: Central, near Shinjuku. Free cancellation available. --- ### **Day 2: April 11, 2025 (Pleasant & Partly Cloudy)** **Morning:** - **Breakfast:** Local breakfast at your hotel or **Sarabeth's** for a delightful brunch. - **Activity:** Visit **Meiji Shrine.** - A peaceful shrine surrounded by a beautiful forest—enjoy leisurely walks. **Time: 8:00 AM – 11:00 AM** --- **Lunch:** - **Location:** **Omotesando Hills** - Explore shops and cafés; grab a bite at the **Café Kitsuné.** **Time: 11:30 AM – 1:00 PM** --- **Afternoon:** - **Activity:** Shopping in **Harajuku** & explore **Takeshita Street.** - Discover quirky shops and stylish street fashion. **Time: 1:30 PM – 3:30 PM** --- **Evening:** - **Live Event:** Attend the **Music Bridge Tokyo 2025** - Check out a variety of performances happening in the evening! **Dinner:** - **Location:** Restaurant nearby the event venue. - Enjoy Tokyo-style sushi or izakaya dining. **Time: 5:00 PM – 8:00 PM** --- **Accommodation:** - **Option:** If staying longer, consider **Park Hotel Tokyo** or **Hotel Century Southern Tower** for convenience and comfort. ### **Weather Overview:** - **April 10:** 20°C (68°F), overcast with showers. 🌧️ - **April 11:** 20°C (68°F), pleasant and partly cloudy. 🌤️ ### **Travel Considerations:** - **Transport:** Use the Tokyo Metro or JR trains for easy commuting. - **Pack an umbrella** for Day 1 and wear comfortable shoes for walking. --- Enjoy your delightful Tokyo trip filled with unique experiences! 🗼✨ === Streaming Complete === ``` ## 🌟 Highlights This cookbook has guided you through setting up and running a dynamic travel planner using **OpenAI Agents** and the **Dappier MCP Server**. By connecting your agent to real-time tools via MCP, you’ve created an assistant capable of generating rich, up-to-date itineraries that adapt to weather, events, and deals. Key components of this workflow include: * **OpenAI Agents SDK**: A powerful toolkit that enables large language models to operate as autonomous agents, use tools, and execute multi-step workflows with memory and structured decision-making. * **Dappier MCP**: A Model Context Protocol server that connects your agents to real-time, rights-cleared, AI-powered tools such as live search, weather, stock data, and content recommendations. * **Dynamic Travel Planning**: A real-world use case where the assistant creates a multi-day itinerary using live weather, events, and hotel data sourced via MCP. This architecture can be adapted to other use cases requiring live data integration, intelligent tool use, and context-aware output generation using the Agents SDK and MCP ecosystem.