Serves real-time and static transit data through a Model Context Protocol (MCP) interface for Large Language Models.
Sponsored
This Python-based MCP server enables Large Language Models to access and interpret GTFS Static and Realtime transit data. It incorporates an intelligent in-memory transit routing engine to facilitate location-aware transit lookups, allowing LLMs to list stations, check upcoming train arrivals, plan future trips, and read service alerts. Its core functionality revolves around parsing GTFS feeds, performing routing with a Connection Scan Algorithm, and exposing this rich data via a standardized interface for intelligent applications.
Características Principales
01Exposes tools natively to Claude and other MCP-compatible clients.
02Parses GTFS static data (stops, routes, trips, stop_times) into memory for efficient lookups.
03Utilizes `gtfs-realtime-bindings` to process Protocol Buffer feeds for Trip Updates and Alerts.
04Includes an in-memory Connection Scan Algorithm (CSA) for optimal transit route planning and filtering.
05Supports multiple transport modes including `stdio`, `sse`, and `streamable-http`.
060 GitHub stars
Casos de Uso
01Querying upcoming train arrivals for specific routes and destinations.
02Planning multi-leg transit itineraries for future dates and times.
03Monitoring service disruptions for particular transit lines or stations.
