Infobus MCP Server

Overview

The Infobus MCP is a Model Context Protocol server designed to bridge the gap between conversational AI assistants and public transit data. By exposing a standardized set of resources, tools, prompts, and sampling endpoints, it allows LLM‑driven chatbots to retrieve real‑time schedules, plan trips, and answer natural language queries about routes, stops, and vehicle status. For developers building transit‑aware assistants, this eliminates the need to write custom integrations for each transport authority and ensures consistent data handling across multiple AI platforms.

Core Problem Solved

Transit information is notoriously fragmented: schedules live on disparate APIs, updates happen in real time, and natural language queries often require complex parsing. Infobus MCP consolidates these challenges into a single protocol surface, enabling developers to focus on conversational design rather than data ingestion. It also guarantees that AI assistants can present accurate, up‑to‑date transit details without exposing the underlying API intricacies to end users.

Key Features & Capabilities

• Trip Planning Tool – Accepts origin, destination, and optional time constraints to compute optimal routes across multiple transit modes.
• Real‑Time Status Queries – Provides live vehicle positions, arrival predictions, and delay alerts.
• Natural Language Assistant Prompt – Supplies context‑aware prompts that help LLMs interpret user intent and format responses in a conversational tone.
• Sampling & Context Management – Allows fine‑tuned control over how much historical data is included in the model’s context, improving response relevance while respecting token limits.

Real‑World Use Cases

• Ride‑hailing and navigation apps that need instant transit suggestions as part of multimodal routing.
• Customer support bots for transportation agencies, delivering schedule confirmations and delay updates.
• Smart city dashboards that embed conversational agents to answer citizen questions about public transport availability.
• Accessibility services providing spoken or text‑based transit information for visually impaired users.

Integration with AI Workflows

Developers can hook Infobus MCP into existing LLM pipelines via the standard MCP client libraries. The server’s prompts and tools are consumed just like any other tool in a chain-of-thought workflow, allowing the model to reason about trip planning before generating an answer. Because the MCP surface is agnostic to the underlying LLM, it works seamlessly with Claude, GPT‑4o, or any future model that supports the protocol.

Unique Advantages

Infobus MCP distinguishes itself with its native support for real‑time transit data and a single, well‑documented interface that abstracts the complexity of multiple transport APIs. Its prompt‑driven design ensures that conversational agents can produce natural, contextually rich responses without manual post‑processing. For developers building transit‑centric AI experiences, this server provides a ready‑made, scalable foundation that accelerates time to market and enhances user trust through accurate, up‑to‑date information.