> ## 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 LangChain + Dappier MCP using `mcp-use` This cookbook demonstrates how to build a real-time, AI-powered travel planner using **LangChain**, **Dappier MCP**, and the lightweight `**mcp-use**` client. By integrating live data through the **Model Context Protocol (MCP)**, this guide walks through how to create structured, tool-augmented agents without using the OpenAI Agents SDK. In this cookbook, you'll explore: * **LangChain + OpenAI**: A modular framework to build LLM-powered applications with support for agents, tools, and memory. * **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. * **mcp-use**: A lightweight Python client that bridges any LLM with any MCP server using standard transports like `stdio` and `http`—without relying on proprietary SDKs. * **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. ## 📦 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: Create a Virtual Environment (Recommended)** Create and activate a virtual environment to isolate dependencies. **macOS / Linux**: ```bash theme={null} python3 -m venv venv source venv/bin/activate ``` **Windows**: ```bash theme={null} python -m venv venv venv\Scripts\activate ``` *** **Step 3: Install Python Packages** Install the necessary Python dependencies including LangChain and `mcp-use`. ```bash theme={null} pip install langchain-openai mcp-use python-dotenv ``` ## 🔑 Setting Up API Keys You’ll need API keys for both **Dappier** and **OpenAI** to authenticate your requests and access tools. *** **Dappier API Key** Visit [Dappier](https://platform.dappier.com/profile/api-keys) to generate your API key. Dappier provides free credits to help you get started. You can set the API key as an environment variable in your terminal: ```bash theme={null} export DAPPIER_API_KEY=your-dappier-api-key ``` Or include it in a `.env` file at the root of your project: ```env theme={null} DAPPIER_API_KEY=your-dappier-api-key ``` *** **OpenAI API Key** Go to [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 include it in your `.env` file: ```env theme={null} OPENAI_API_KEY=your-openai-api-key ``` In your Python script, load the `.env` file: ```python Python theme={null} from dotenv import load_dotenv load_dotenv() ``` ## ⚙️ Import Dependencies Start by importing the required modules to build the travel planner agent. This includes components from `mcp-use`, LangChain, and environment configuration. ```python Python theme={null} import asyncio import os from dotenv import load_dotenv from langchain_openai import ChatOpenAI from mcp_use import MCPAgent, MCPClient ``` These imports enable: * Loading API keys from the environment using `dotenv` * Running asynchronous workflows with `asyncio` * Accessing OpenAI models via LangChain * Connecting to the Dappier MCP server and executing tool-augmented queries using `mcp-use` ## 📝 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 MCP agent using `mcp-use`, formulates the travel planning query, and executes it using real-time tools provided by the Dappier MCP server. ```python Python theme={null} async def run_travel_planner(city: str, num_days: str, travel_date: str): # Load environment variables load_dotenv() # Retrieve API key api_key = os.getenv("DAPPIER_API_KEY") if not api_key: raise ValueError("DAPPIER_API_KEY is not set") # MCPClient configuration using stdio transport (via uvx dappier-mcp) config = { "mcpServers": { "dappier": { "command": "uvx", "args": ["dappier-mcp"], "env": { "DAPPIER_API_KEY": api_key } } } } # Create MCP client and LLM client = MCPClient.from_dict(config) llm = ChatOpenAI(model="gpt-4o") # Create the agent agent = MCPAgent(llm=llm, client=client, max_steps=30) try: # Formulate query 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. Output: Present a detailed {num_days}-day itinerary, including timing, activities, booking links, weather information for each day and travel considerations. """ print("\n" + "-" * 40) print(f"Running itinerary generation for {city} starting {travel_date} for {num_days} days") print("\n=== Response ===\n") result = await agent.run(query, max_steps=30, Verbose=True) print(result) finally: # Clean up sessions if client.sessions: await client.close_all_sessions() ``` ## 🚦 Initialize and Launch the Workflow The `main()` function collects user input, then launches the asynchronous workflow to run the travel planner using `mcp-use` and Dappier MCP. ```python Python theme={null} async def main(): city, num_days, travel_date = get_user_input() await run_travel_planner(city, num_days, travel_date) ``` To start the planner, run the main function using `asyncio`: ```python Python theme={null} if __name__ == "__main__": asyncio.run(main()) ``` ```json theme={null} Enter the city for your travel: london Enter the number of days for the trip: 5 Enter the start date of travel: today ---------------------------------------- Running itinerary generation for london starting today for 5 days === Response === [04/23/25 13:16:16] INFO Processing request of type ListToolsRequest server.py:534 INFO Processing request of type ListToolsRequest server.py:534 [04/23/25 13:16:22] INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:16:25] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15 "HTTP/1.1 200 OK" _client.py:1025 [04/23/25 13:16:29] INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:16:37] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15 "HTTP/1.1 200 OK" _client.py:1025 INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:16:45] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15 "HTTP/1.1 200 OK" _client.py:1025 INFO Processing request of type CallToolRequest server.py:534 [04/23/25 13:16:51] INFO HTTP Request: POST https://api.dappier.com/app/aimodel/am_01j06ytn18ejftedz6dyhz2b15 "HTTP/1.1 200 OK" _client.py:1025 Here's a detailed 5-day travel itinerary for your trip to London, tailored to the real-time weather forecast and live events from April 23 to April 27, 2025. 🌟 --- ### Day 1: April 23, 2025 (Wednesday) - **Weather**: Cloudy with rain, especially in the morning. High 55°F (13°C) / Low 47°F (8°C). - **Suggested Activities**: - **St George's Day Celebrations**: Enjoy various events including Morris dancing and whisky tasting across the city. 🇬🇧 - **The Queen of the Night Show**: Attend this tribute to Whitney Houston. 🎤 - **Hotel**: Check into the Strand Palace for comfort and convenience. ### Day 2: April 24, 2025 (Thursday) - **Weather**: Overcast with possible sprinkles. High 62°F (17°C) / Low 45°F (7°C). - **Suggested Activities**: - Explore local venues for various concerts and live music. 🎶 - **Shopping**: Visit Covent Garden and enjoy the nearby Resident Covent Garden Hotel for style and comfort. ### Day 3: April 25, 2025 (Friday) - **Weather**: Mainly cloudy. High 64°F (18°C) / Low 42°F (6°C). - **Suggested Activities**: - **Theatre Shows**: Catch a classic or modern production on the West End. 🎭 - **Hotel**: Consider staying at the Royal National Hotel for a budget-friendly option. ### Day 4: April 26, 2025 (Saturday) - **Weather**: Drizzly skies. High 62°F (17°C) / Low 49°F (9°C). - **Suggested Activities**: - **Vivaldi by Candlelight**: Attend a stunning classical music experience. 🎻 - **Explore Local Live Music**: Visit nearby gigs for a night out. - **Hotel**: Book the Corbigoe Hotel with prices starting at $64/night. ### Day 5: April 27, 2025 (Sunday) - **Weather**: Mostly cloudy, chance of light rain. High 60°F (16°C) / Low 48°F (9°C). - **Suggested Activities**: - **London Marathon**: Watch or even participate in this iconic event. 🏃‍♀️🏃‍♂️ - **Theatre and Concerts**: End your trip with a flourish by catching another live performance. - **Hotel**: Relax at St Athans Hotel, known for its cozy setting. --- **Travel Considerations:** - **Hotel Booking**: Most hotels offer flexible options—consider locking in deals on Orbitz for exclusive offers and cancellation policies. - **Packing**: Bring a light jacket, umbrella, and comfortable shoes for exploring the city. Enjoy your spectacular 5-day trip to London filled with culture, entertainment, and cozy accommodations! 🌆✨ ``` ## 🌟 Highlights This cookbook has guided you through building a dynamic travel planner using **LangChain**, **Dappier MCP**, and the `**mcp-use**` Python client. By connecting your agent to real-time tools via MCP, you’ve created an assistant capable of generating up-to-date travel itineraries based on live weather, events, and hotel deals. Key components of this workflow include: * **LangChain + OpenAI**: A modular framework for creating LLM-powered applications with agent capabilities. * **Dappier MCP**: A Model Context Protocol server that enables access to live, rights-cleared data through AI tools like real-time search, weather, and finance. * **mcp-use**: A lightweight open-source client to connect any LLM to any MCP server using `stdio` or `http` transports, without vendor lock-in. This architecture can be extended to other real-time applications requiring dynamic data, tool use, and intelligent orchestration powered by the MCP ecosystem.