Google Flights MCP Server

The Google Flights MCP server bridges the gap between AI assistants and real‑time flight information by exposing a suite of scraping tools that query Google Flights directly. For developers building travel‑planning or cost‑comparison features into conversational agents, this server eliminates the need to maintain custom flight APIs or pay for third‑party data feeds. Instead, it leverages the open‑source fast_flights library to programmatically navigate Google Flights’ interface, extract itineraries, and return structured results that an AI can consume immediately.

At its core the server offers three primary tools. retrieves one‑way flight options for a single departure date, allowing agents to suggest quick outbound trips. expands this capability to round‑trip itineraries with distinct departure and return dates, supporting travel planning for vacations or business trips. Finally, scans an entire date window to surface the best round‑trip options, optionally filtering by stay duration or returning only the cheapest flight per date pair. Each tool accepts common parameters such as origin, destination, passenger count, and seat class, making them intuitive to call from an MCP‑enabled client.

Developers benefit from the server’s seamless integration with existing MCP workflows. By adding a single configuration entry to their client (e.g., Cline or Claude Desktop), the assistant can invoke any of these tools as if they were native commands. The server communicates over STDIO, which is natively supported by most MCP frameworks, ensuring low latency and straightforward deployment. Because the tools are wrapped in a stateless API, they can be scaled horizontally or run as lightweight Docker containers without additional state management.

Real‑world use cases abound. A travel chatbot can offer instant flight price comparisons across multiple dates, automatically updating the user as new fares appear. A corporate expense tool could recommend cost‑effective itineraries that respect employee travel policies (e.g., minimum stay days). Even a data‑analysis pipeline could batch‑query flight windows to feed demand forecasting models. The server’s ability to return only the cheapest options reduces bandwidth and processing overhead, which is critical for high‑volume applications.

Unique advantages of this MCP server include its reliance on the proven fast_flights scraper, which avoids the pitfalls of proprietary APIs and keeps pricing data free. Its modular toolset allows developers to pick exactly the functionality they need, from single‑date queries to exhaustive range scans. Additionally, the server’s error handling and optional resource‑intensive flags (e.g., ) give fine control over performance, enabling smooth operation even when network conditions fluctuate. In short, the Google Flights MCP server empowers AI assistants to deliver accurate, up‑to‑date travel information without the overhead of managing external flight data services.