PTV Transit MCP Server

An MCP server that exposes Victoria's public transport (PTV) GTFS timetable data — trains, trams, and buses — so an LLM (Claude Desktop, Cursor, etc.) can answer natural-language questions about routes, stops, and departure times.

Data at a glance: 1,069 routes · 31,971 stops · 333,875 trips · 12.6 million scheduled stop times, across 8 transport modes (metro train, metro tram, metro bus, regional train, regional bus, regional coach, night bus, SkyBus).

Why this project

Data source: Transport Victoria's GTFS Schedule dataset, published by the Victorian Department of Transport and Planning. It contains static timetable information for all metropolitan and regional trains, buses (including coach), and trams in Victoria, refreshed on a weekly (or as-needed) basis.

This project turns that raw GTFS export into a queryable SQLite database and wraps it in an MCP server with:

• Tools for structured lookups (stop search, route listing, next departures)
• A guarded raw-SQL tool for open-ended questions, restricted to read-only SELECT statements with a hard file-level read-only guarantee underneath
• A schema resource so the model can write informed queries without guessing column names
• A prompt template for trip planning

Setup (Windows, using uv)

1. Install uv if you don't have it:

   powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
   

2. Clone this repo and install dependencies:

   git clone https://github.com/MananshuMandaliya/ptv-mcp-server.git
   cd ptv-mcp-server
   uv sync
   

3. Download the GTFS data: Go to the GTFS Schedule dataset page, download the current GTFS.zip, and save it as:

   data/gtfs.zip
   

4. Build the database:

   uv run python scripts/build_database.py
   

   This creates data/ptv.db. It can take a couple of minutes — stop_times alone typically runs into the millions of rows across all modes.

5. Test it with the MCP Inspector:

   uv run mcp dev src/ptv_mcp/server.py
   

   This opens an interactive UI in your browser where you can call each tool directly and see the raw responses.

6. Install into Claude Desktop:

   uv run mcp install src/ptv_mcp/server.py
   

   Restart Claude Desktop, and you should see this server's tools available in a new conversation.

Example queries once connected

• "What time is the next train from Oakleigh station?"
• "List all tram routes."
• "How many stops does the Frankston line have?" (via the raw SQL tool)

Project structure

ptv-mcp-server/
├── pyproject.toml
├── scripts/build_database.py   # GTFS.zip -> SQLite
└── src/ptv_mcp/server.py       # the MCP server

Design decisions

Why SQLite instead of a hosted database? Zero setup for anyone reviewing this project — clone, build, run. No credentials, no hosting cost, no server to keep alive. GTFS is also a natural fit for SQLite: it's a static, read-heavy dataset that's rebuilt from source on a schedule rather than written to at runtime.

Why a nested-zip loader? Victoria's GTFS.zip isn't structured as one folder per mode with plain .txt files — each branch folder (train, tram, bus, etc.) contains its own nested google_transit.zip. The first version of the loader assumed flat .txt files and silently loaded zero rows. Rather than guessing at a fix, I wrote a small diagnostic script to print the actual zip structure, confirmed the nested-zip pattern, then rewrote the loader to open each nested zip in memory (io.BytesIO) rather than extracting to disk.

Why guard run_sql_query at two layers? The tool needs to let the model run arbitrary read-only SQL for questions the structured tools don't anticipate, but "arbitrary SQL from an LLM" is a real risk surface. So there are two independent layers: the SQLite connection itself is opened via a read-only URI (file:...?mode=ro), which SQLite enforces at the file-handle level regardless of what the query says, and a regex guard rejects anything that isn't a single SELECT statement before it's even executed. Either layer alone would probably be enough; both together means one bug in the regex doesn't turn into a write.

Why index stop_id, trip_id, route_id, and service_id? stop_times has 12.6 million rows. Without indexes, get_next_departures was a full table scan on every call. With them, lookups return in well under a second even on this dataset size.

Notes / limitations

• This serves the static schedule, not live GPS positions or real-time delays (Transport Victoria separately publishes a GTFS-Realtime feed for that — a natural extension of this project).
• The GTFS export contains a rolling window of timetable data from its export date, so data/gtfs.zip should be re-downloaded periodically to stay current.