Plans Madurai CityBus routes, checks TNSTC fares, searches bilingual stops, and reports platform bay details.
Copy the AI prompt to install this server into Claude Code, Cursor, or another agent β or use 1-click editor setup below.
We ran the install command below but it didn't respond within our test window β this can mean a slow first-time install rather than a real problem.
npx -y madurai-transit-mcpNo response to initialize.
This is an experimental automated check and can have false negatives β missing environment variables, a slow cold install, etc. It doesnβt necessarily mean somethingβs wrong. Last checked 14d ago.
π‘ Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Inspect callable tools, capabilities, and parameters exposed to AI agents by Madurai Transit MCP.
The rsiva2294/madurai-transit-mcp MCP server exposes Madurai CityBus data to MCP-compatible AI assistants. It covers route discovery, bus stop lookup, route-specific stop sequences, official TNSTC stage fares, and departure bay information for Periyar Bus Stand. The dataset describes 966 bus routes and approximately 3,000 stops.
Route planning supports a direct journey or one transfer. Stop searches accept English names, Tamil Unicode, and colloquial transliterations, which is useful when the same place is known by multiple spellings. Results can also include canonical maduraione.in route links for viewing visual route maps.
This belongs in the π Travel & Transportation category for applications that answer local Madurai public-transit questions. It is suited to trip-planning assistants, stop-finding interfaces, and fare lookup workflows rather than systems that require a complete real-time timetable.
The package is launched as an MCP process with npx. An MCP client sends structured tool calls containing locations, stop names, or bus numbers, and the server returns transit data for the requested operation.
Route searches take an origin, destination, and transfer limit. Bus-detail requests use a bus number and return route information, service classification, platform bay data, and the full stop sequence. Stop searches use a text query and can return stop classifications and GPS coordinates. Fare calculations use the boarding stop, alighting stop, and bus number to determine stage fares across the supported TNSTC service classes.
Transfer guidance follows a seven-spoke Madurai topological model. That model helps identify interchange options, but it should not be treated as a guarantee of a particular live departure.
The rsiva2294/madurai-transit-mcp MCP server can be started with:
For Claude Desktop, add an MCP server entry whose command is npx and whose arguments are -y and madurai-transit-mcp in the platform-specific claude_desktop_config.json file. The documented locations are under the Claude application support directory on macOS, %APPDATA% on Windows, and .config in the Linux home directory.
Cursor can use the same command and arguments in .cursor/mcp.json or through Cursor Settings under Features and MCP. The MCP Inspector can run the package for interactive local testing with:
No required environment variables or API keys are specified in the provided setup instructions.
search_bus_routes finds direct and one-transfer journeys between two Madurai locations.get_bus_details returns details for a bus number, including its stop sequence, service category, and departure bay where available.search_bus_stops searches stop names in English or Tamil and provides spoke classifications and verified coordinates where available.calculate_fare calculates official TNSTC stage fares for Ordinary, LSS, Express, and Deluxe AC services using boarding and alighting stops plus a bus number.maduraione.in/route/... links for visual route views.TNSTC Madurai services use shift-based headway frequency rather than consistently published minute-by-minute digital timetables. The server therefore should not be used as a promise of an exact departure time.
Coordinate coverage is uneven. Roughly 600 core urban stops have verified survey coordinates; other rural and peri-urban stops are represented topologically by their sequence. Transfer suggestions are guidance based on the seven-spoke model, so passengers should confirm current departures and platforms at the relevant bus stand.
Platform bay information is specifically described for Periyar Bus Stand, with bays identified as I, II, III, and IV. The provided material does not establish equivalent platform coverage for every bus stand. The project is licensed under MIT and identifies MaduraiOne as its provider.
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