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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.

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📦 Installation

To get started, install the required tools and dependencies: Step 1: Install uv (required to run the Dappier MCP server) macOS / Linux: 
curl -LsSf https://astral.sh/uv/install.sh | sh
Windows: 
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
Step 2: Install Python Packages Install OpenAI Agents SDK. 
pip install openai-agents

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🔑 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 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: 
export DAPPIER_API_KEY=your-dappier-api-key
Or programmatically in your Python script:
Python
import os

os.environ["DAPPIER_API_KEY"] = "your-dappier-api-key"
OpenAI API Key Visit OpenAI to retrieve your API key. Set it in your terminal: 
export OPENAI_API_KEY=your-openai-api-key
Or in your Python script:
Python
os.environ["OPENAI_API_KEY"] = "your-openai-api-key"

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⚙️ 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
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

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📝 Define User Input

We’ll collect basic trip preferences from the user: city, number of days, and travel start date.
Python
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

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🛰️ 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
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")

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🚦 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
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)

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🧪 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
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())

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 ===

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🌟 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.