Where's my train? MTA Guide

While most commuters rely on standalone apps like Transit or Citymapper, this project targets the emerging 'headless' software market. By utilizing the Model Context Protocol, it allows LLMs (such as Claude) to act as a personal transit concierge, answering natural language questions about train delays or arrival times without the user needing to switch between different apps. The primary value proposition is the seamless bridge between real-time urban infrastructure data and generative AI. Instead of checking a map, a user can simply ask their AI assistant, 'When should I leave for the L train given the current delays?' This represents a shift toward more integrated, context-aware productivity tools for urban professionals. However, the tool's accessibility is its biggest hurdle. Because it is an MCP server hosted on GitHub, it requires a local environment (like Node.js) and a compatible AI client to function. This isn't a 'download and go' mobile app for the average commuter; it is a developer-centric utility that demands a specific technical setup. For NYC-based power users who spend their workday within AI-enhanced IDEs or desktop assistants, this is a niche but high-utility tool. It excels at providing high-fidelity, official MTA data in a format that AI can reason with, making it an excellent example of how the 'Internet of Things' is finally meeting 'Large Language Models.'