🏡 CAMEL-AI + MCP: Airbnb Use Case

This example demonstrates how to use the CAMEL-AI OWL framework and MCP (Model Context Protocol) to search for Airbnb listings using a custom MCP server (@openbnb/mcp-server-airbnb). Agents in the OWL framework coordinate to perform tool-augmented travel research in a structured, automated way.

✨ Use Case

“Find me the best Airbnb in Gurugram with a check-in date of 2025-06-01 and a check-out date of 2025-06-07 for 2 adults. Return the top 5 listings with their names, prices, and locations.”

Agents leverage an MCP server to execute real-time Airbnb queries and return formatted results.

📦 Setup

1. Clone and install dependencies

git clone https://github.com/camel-ai/owl
cd owl
pip install -r requirements.txt

2. Configure MCP Server

In your mcp_servers_config.json, add the following:

{
  "mcpServers": {
    "airbnb": {
      "command": "npx",
      "args": [
        "-y",
        "@openbnb/mcp-server-airbnb",
        "--ignore-robots-txt"
      ]
    }
  }
}

🛠️ You will need Node.js and NPM installed. Run npx will automatically fetch the Airbnb MCP server.

3. Run the Example Script

python community_usecase/Airbnb_MCP

You can also customize the prompt inside the script itself. Edit the default_task section of Airbnb_MCP.py like this:

# Replace this line:
default_task = (
    "here you need to add the task"
)

# Example:
default_task = (
    "Find me the best Airbnb in Gurugram with a check-in date of 2025-06-01 "
    "and a check-out date of 2025-06-07 for 2 adults. Return the top 5 listings with their names, "
    "prices, and locations."
)

This allows agents to work from your hardcoded task without passing anything via command line.

🧠 How It Works

MCPToolkit reads the config and connects to the @openbnb/mcp-server-airbnb.
OWL RolePlaying Agents simulate a conversation between a content_curator and a research_assistant.
The assistant agent calls the MCP Airbnb server to fetch listings.
The results are processed, formatted, and printed.

🚧 Notes

This script uses GPT-4o via OpenAI for both user and assistant roles.
Supports async execution and graceful cleanup of agents and MCP sessions.
Add retries and fallback logic for production use.

README.md