Airbnb
The open-source Airbnb MCP Server is designed for developers who need to interact with Airbnb’s vast database of listings.
It simplifies the process of searching for Airbnb properties and retrieving detailed listing information.
This MCP server operates without requiring an API key, making it accessible for various projects and applications.
Features List
- 🔍 Comprehensive search functionality for Airbnb listings
- 📊 Detailed property information retrieval
- 🤖 Respects Airbnb’s robots.txt rules (with option to override)
- 🛠️ Built with Node.js and Cheerio for efficient HTML parsing
- 📡 Returns structured JSON data for easy integration
- 🧩 Reduces context load through data flattening and selective picking
Use Cases
- Travel planning applications that need to aggregate Airbnb options alongside other accommodations
- Price comparison tools for vacation rentals across different platforms
- Data analysis projects examining Airbnb pricing trends in specific locations
- Booking management systems that need to pull in Airbnb listing details
How to Use It
- Ensure Node.js is installed on your system.
- Open Claude Desktop and navigate to Settings > Developer > Edit Config.
- Add the following to your
claude_desktop_config.json:
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": [
"-y",
"@openbnb/mcp-server-airbnb"
]
}
}
}- To ignore robots.txt for all requests, use this alternative configuration:
{
"mcpServers": {
"airbnb": {
"command": "npx",
"args": [
"-y",
"@openbnb/mcp-server-airbnb",
"--ignore-robots-txt"
]
}
}
}- Restart Claude Desktop to apply the changes.
Usage
The Airbnb MCP Server provides two main functions:
airbnb_search: Search for Airbnb listings
- Required input:
location(string) - Optional inputs:
placeId(string)checkinandcheckout(string, YYYY-MM-DD format)adults,children,infants,pets(numbers)minPriceandmaxPrice(numbers)cursor(string)ignoreRobotsText(boolean)
airbnb_listing_details: Get detailed information about a specific listing
- Required input:
id(string) - Optional inputs:
checkinandcheckout(string, YYYY-MM-DD format)adults,children,infants,pets(numbers)ignoreRobotsText(boolean)
Both functions return structured JSON data containing the requested information.
FAQs
Q: Is this MCP Server officially affiliated with Airbnb?
A: No, this is an independent tool and is not officially associated with or endorsed by Airbnb.
Q: How does the server handle rate limiting or blocking from Airbnb?
A: The server respects Airbnb’s robots.txt rules by default. However, it’s important to use the tool responsibly to avoid potential IP blocking.
Q: Can I use this server for commercial applications?
A: The server is licensed under the MIT License, which generally allows for commercial use. However, you should review Airbnb’s terms of service to ensure your specific use case complies with their policies.
Q: How current is the data returned by the server?
A: The server fetches data in real-time from Airbnb’s website. However, there may be a slight delay between updates on Airbnb and when they become available through this server.
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FAQs
Q: What exactly is the Model Context Protocol (MCP)?
A: MCP is an open standard, like a common language, that lets AI applications (clients) and external data sources or tools (servers) talk to each other. It helps AI models get the context (data, instructions, tools) they need from outside systems to give more accurate and relevant responses. Think of it as a universal adapter for AI connections.
Q: How is MCP different from OpenAI's function calling or plugins?
A: While OpenAI's tools allow models to use specific external functions, MCP is a broader, open standard. It covers not just tool use, but also providing structured data (Resources) and instruction templates (Prompts) as context. Being an open standard means it's not tied to one company's models or platform. OpenAI has even started adopting MCP in its Agents SDK.
Q: Can I use MCP with frameworks like LangChain?
A: Yes, MCP is designed to complement frameworks like LangChain or LlamaIndex. Instead of relying solely on custom connectors within these frameworks, you can use MCP as a standardized bridge to connect to various tools and data sources. There's potential for interoperability, like converting MCP tools into LangChain tools.
Q: Why was MCP created? What problem does it solve?
A: It was created because large language models often lack real-time information and connecting them to external data/tools required custom, complex integrations for each pair. MCP solves this by providing a standard way to connect, reducing development time, complexity, and cost, and enabling better interoperability between different AI models and tools.
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A: Security is a major consideration. While MCP includes principles like user consent and control, risks exist. These include potential server compromises leading to token theft, indirect prompt injection attacks, excessive permissions, context data leakage, session hijacking, and vulnerabilities in server implementations. Implementing robust security measures like OAuth 2.1, TLS, strict permissions, and monitoring is crucial.
Q: Who is behind MCP?
A: MCP was initially developed and open-sourced by Anthropic. However, it's an open standard with active contributions from the community, including companies like Microsoft and VMware Tanzu who maintain official SDKs.
