The mcp-server-airbnb is an MCP server designed to provide AI models with access to Airbnb data, enabling applications like travel planning and personalized recommendations. It offers two key tools: airbnb_search for finding listings based on location and other criteria, and airbnb_listing_details for retrieving comprehensive information about specific properties.

This server interacts seamlessly with AI models by providing structured JSON data, streamlining data processing and reducing context load. Notably, it operates without requiring an Airbnb API key, respecting robots.txt rules while scraping data using cheerio. Developers can easily integrate this server into their workflows, as demonstrated by the provided Claude Desktop setup instructions. The core value lies in unlocking Airbnb's vast dataset for AI-driven innovation, simplifying data acquisition and enhancing model capabilities in the travel and hospitality domain. It can be installed via npx or Smithery.

Airbnb Listing Search

The airbnb_search tool is a core feature, enabling AI models to dynamically query Airbnb listings based on a variety of criteria. It accepts a location string as a mandatory input, allowing the AI to focus its search on a specific geographic area. Optional parameters such as check-in/check-out dates, number of guests (adults, children, infants, pets), and price range (minPrice, maxPrice) provide further refinement of the search. The cursor parameter enables pagination, allowing the AI to retrieve large datasets of listings in manageable chunks. The tool returns an array of listings, each containing key details such as name, price, and location, formatted as structured JSON data.

For example, an AI-powered travel assistant could use this tool to find suitable Airbnb accommodations for a user planning a family vacation to Paris in July, specifying the number of adults and children, desired price range, and preferred amenities. The AI can then present the user with a curated list of options based on their preferences.

Detailed Listing Information

The airbnb_listing_details tool complements the search functionality by providing in-depth information about a specific Airbnb listing. It requires the listing's unique ID as input and returns a comprehensive JSON object containing details such as the listing description, host information, available amenities, and pricing breakdown. Optional parameters like check-in/check-out dates and the number of guests can be included to retrieve pricing specific to those dates. This tool allows AI models to understand the nuances of a listing beyond the basic search results, enabling more informed decision-making.

Consider an AI model designed to provide personalized travel recommendations. After using the airbnb_search tool to identify potential listings, it can use airbnb_listing_details to gather more granular information about each listing, such as the quality of the host based on reviews, the availability of specific amenities like a workspace or a fully equipped kitchen, and the detailed pricing structure including cleaning fees and service fees. This detailed information allows the AI to provide more accurate and relevant recommendations to the user.

Robots.txt Compliance

A crucial feature of the mcp-server-airbnb is its adherence to Airbnb's robots.txt rules. This ensures that the server operates ethically and responsibly by respecting Airbnb's crawling policies. Before accessing any page, the server checks the robots.txt file to determine whether it is permitted to crawl that specific URL. This prevents the server from overloading Airbnb's servers with excessive requests and ensures compliance with legal and ethical standards. The option to ignore robots.txt is available via the --ignore-robots-txt flag, but should only be used in development or testing environments, or when explicit permission has been granted.

This feature is particularly valuable for developers building AI applications that rely on web scraping. By automatically handling robots.txt compliance, the mcp-server-airbnb simplifies the development process and reduces the risk of being blocked by Airbnb. It also demonstrates a commitment to ethical data collection practices, which is increasingly important in the age of data privacy and responsible AI.

Data Flattening and Picking

The mcp-server-airbnb server reduces context load by flattening and picking data. This optimization is crucial for AI models with context windows, as it minimizes the amount of irrelevant information passed to the model. By extracting only the most pertinent details from the HTML structure and presenting them in a structured JSON format, the server reduces the token count required to represent the information. This allows the AI model to process more information within its context window, leading to improved performance and accuracy.

For example, instead of sending the entire HTML content of an Airbnb listing page to the AI model, the server extracts key details such as the listing title, price, description, and amenities, and presents them in a flattened JSON structure. This reduces the amount of irrelevant HTML markup and styling information that the AI model needs to process, allowing it to focus on the core information relevant to its task. This optimization is particularly beneficial for large-scale data analysis and machine learning applications.