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Model Context Protocol Beta

Connect your AI agent to global transit data

Our MCP server connects Claude, ChatGPT, Gemini, Cursor, and any other MCP client to the busmaps.com Transit API. The agent plans routes, finds stops, and reads real-time departures across 90+ countries, and works out the tools and their schemas on its own.

Connect now Browse the REST API

What is MCP?

The Model Context Protocol is an open standard that connects AI assistants to external tools and data. Instead of pasting API documentation into a chat and hoping the model guesses the right parameters, an MCP server publishes a typed list of tools. The AI reads that list and calls the tools on its own. Connect the busmaps.com server once and your assistant queries the transit API directly, without you re-explaining it.

What your agent can do

Eleven read-only tools for trip planning, live data, geography, and geocoding.

plan_transit_route

Plans a journey between two points by bus, subway, train or tram. Returns route options with legs, board and alight stops, times, transfers, an encoded polyline, and predicted times where live data exists. Handles cross-border trips such as London to Paris by Eurostar.

get_realtime_departures

Returns scheduled and live departures from stops near a point or from a specific stop. Delay is rtDepartureTime minus the scheduled departureTime.

find_nearby_stops

Returns transit stops within a radius of a point, each with its id, name, coordinates, and the lines that serve it.

track_vehicles_in_area

Returns live vehicle positions inside a bounding box, with route id, coordinates, direction, and timestamp.

plan_pedestrian_route

Returns a walking route with turn-by-turn directions from OSRM for the first and last mile to a stop.

pedestrian_distance_matrix

Returns a walking time and distance matrix between multiple points, so the agent can compare which stop is closest on foot.

get_gtfs_feeds_catalog

Returns the catalog of GTFS feeds with download links, licensing, and error-correction status.

photon_search

Geocodes free text into GeoJSON, tuned for autocomplete and typeahead as the user types.

photon_reverse

Resolves coordinates back to a place as GeoJSON, tuned for the same autocomplete-style lookups.

nominatim_search

Geocodes free text into a full structured postal address as JSON.

nominatim_reverse

Resolves coordinates back to a full structured postal address as JSON.

Built to be read by agents

On connect the agent reads the instructions, resources, and schemas, then builds correct requests on its own.

Instructions on connect

The server returns instructions that point the agent to where it should start.

busmaps://start-here, busmaps://recipes

start-here covers the overview and gotchas like coordinate order. recipes gives screen recipes for a departures board, route planner, search autocomplete, stops on a map, and a live vehicles map, each with its tool sequence and caching and debounce patterns.

busmaps://schema/<tool>, busmaps://example/<tool>

Each tool exposes its response JSON Schema and a trimmed real example, so the agent knows the response shape before it calls.

build_transit_app, explain_api

build_transit_app turns one request into an app build plan. explain_api describes the API.

Generated from the contract

Tools are read-only and generated from the same API contract that drives the docs, so the tools and schemas stay in sync with the live API.

No docs to paste

The agent reads the 11 tools, 24 resources, and 2 prompts itself, so nobody pastes documentation into a chat.

Connect

Point your AI client at the server URL - no Docker, no local install. Pick your client below.

Server URL: https://mcp.busmaps.com/mcp

Claude Code

claude mcp add --transport http busmaps-transit https://mcp.busmaps.com/mcp

Config file (Claude Desktop, Cursor, Windsurf, Cline)

{
  "mcpServers": {
    "busmaps-transit": {
      "type": "http",
      "url": "https://mcp.busmaps.com/mcp"
    }
  }
}

Gemini CLI

gemini mcp add --transport http busmaps-transit https://mcp.busmaps.com/mcp

Codex CLI (~/.codex/config.toml)

[mcp_servers.busmaps-transit]
url = "https://mcp.busmaps.com/mcp"

VS Code (GitHub Copilot)

code --add-mcp '{"name":"busmaps-transit","type":"http","url":"https://mcp.busmaps.com/mcp"}'

Ask in plain language

Connect an agent to the server and try these. The agent picks the tool on its own.

  • "Plan a route from central London to central Paris"plan_transit_route returns a Eurostar itinerary with legs, board and alight stops, times across two time zones, and an encoded polyline to draw on a map.
  • "Find transit stops within 300m of 51.5074,-0.1278"find_nearby_stops returns stop ids, names, coordinates, and the lines serving each stop inside the radius.
  • "When is the next departure near Manchester Piccadilly, and is it delayed?"get_realtime_departures returns scheduled and live predicted times. Delay is rtDepartureTime minus departureTime.
  • "Show live vehicles in the area 51.45,-0.25,51.55,-0.05"track_vehicles_in_area returns live positions inside the bounding box with route id, coordinates, direction, and timestamp.
  • "How do I read real-time delays from departures?"The agent answers from the tool description and schema. Nobody pastes documentation into the chat.

Build transit-aware AI agents

Point your AI client at the server and start asking - it works out the tools on its own.

Browse the REST API