Bengaluru BMTC MCP Server

The Bengaluru BMTC MCP Server turns the vast, real‑time transit data of Bangalore’s Metropolitan Transport Corporation into a ready‑to‑consume, Model Context Protocol (MCP) service. By exposing bus locations, route schedules, stop details and ETA calculations through a standardized API, the server resolves a common pain point for AI assistants: accessing live public‑transport information without bespoke integrations or manual scraping. Developers can now embed accurate, up‑to‑date bus data directly into conversational agents, enabling features such as route planning, live arrival notifications and travel time estimation in a single, consistent interface.

At its core, the server follows a clean, modular architecture. The API layer offers REST endpoints for authentication, routes, stops, bus locations and ETA queries. Behind it, the service layer encapsulates business logic—calculating ETAs from real‑time GPS feeds, normalising data from BMTC’s own API and handling geospatial queries. Data persistence is handled by a MongoDB datastore accessed through Mongoose, while a Redis cache sits in front of hot endpoints to reduce latency and limit external API calls. An additional external integration layer pulls raw data from BMTC’s official feeds, ensuring the MCP remains synchronized with the city’s transit network.

Key capabilities include:

• Real‑time bus tracking for over 2,200 routes and 8,400 stops across Bengaluru.
• ETA computation per stop or per route, giving users precise arrival windows.
• Geospatial queries that return nearby stops or buses based on latitude/longitude and radius, enabling location‑aware assistants.
• Authentication & authorization via JWTs, ensuring secure access for both public and private use cases.
• Caching with Redis to deliver sub‑second responses even under high load.

Typical use cases span a wide spectrum: a city‑wide travel chatbot can recommend the quickest bus to a destination, an on‑device navigation app can overlay live bus positions onto maps, and city planners can ingest aggregated route data for performance analysis. In an AI workflow, the MCP server becomes a plug‑in that any Claude or other assistant can call to enrich conversations with up‑to‑date transit facts, eliminating the need for custom data pipelines or manual updates.

What sets this server apart is its tight coupling with BMTC’s official API, ensuring data fidelity, while the MCP façade abstracts away complexities such as authentication and caching. Developers benefit from a single, well‑documented endpoint surface that can be called via the MCP client or directly in chat, making it a powerful backbone for any application that needs reliable public‑transport intelligence.