🚄 RailInfo MCP Server

An integration-ready Model Context Protocol (MCP) server providing real-time Indian Railways information. It gives AI models (like ChatGPT or Claude) the ability to fetch live train running status, station schedules, route maps, crossings, and upcoming train arrivals/departures with high accuracy.

🌟 Features & Tools

1. Live Train Status (get_live_train_status)

Get the current running status of any Indian Railways train.

• Flexible Date Resolution: Supports querying by keywords like "today", "yesterday", or specific dates (e.g., "03-June-2026").

• Target Station Focus: Query a train relative to a specific station (e.g., "When will 12357 reach Varanasi (BSB)?").

  • Remaining Track Distance: Calculates track distance remaining and number of stops to go.

  • Physical GPS Distance: Calculates straight-line physical distance to the station using the Haversine formula based on active GPS coordinates.

  • Remaining Stops Table: Lists upcoming stops with expected arrival time, platform, delay, and current status.

2. Live Station Departures (get_trains_at_station)

Get all trains arriving or departing at a station in the next 2 or 4 hours (matching NTES departures board).

• Hybrid Search Algorithm: Merges static scheduled timetables with active live trains operating within a 120 km radius of the station. This ensures delayed or rescheduled trains are never missed.

• Multi-Instance Handling: Disambiguates between yesterday's delayed train and today's on-time train running concurrently.

3. Train Crossings & Radar (get_train_crossings_and_radar)

Get a "radar view" along a train's active route.

• Oncoming Crossings: Lists oncoming trains scheduled to pass by on opposing tracks.

• Section Traffic (Radar): Identifies other trains running directly ahead or behind in the same block section. Helpful for predicting signal-related delays.

4. Trains Between Stations (get_trains_between_stations)

Find all upcoming trains running from a source station (e.g., ALJN) to a destination station (e.g., NDLS).

• Live Schedule Aggregator: Fetches live departure/arrival times, delay status, and platform numbers at both stations.

5. Train Timetable (get_train_timetable)

Fetch the complete scheduled route/timetable of any train.

• Full Stops List: Lists every single scheduled stop along the route, showing scheduled arrival/departure times, distance (km), platform number, and live expected arrival/departure times if running.

6. Train Route Map (get_train_route_map)

Get the precise geographic coordinates of a train's entire route.

• Station Coordinates: Look up latitude/longitude for every stop on the train's route.

• Map Integration: Generates clickable Google Maps links for every station to plot or visualize the path.

7. Trains Approaching Station (Radar) (get_trains_approaching_station)

Get active, live trains physically approaching a station within a specified radius (default 50 km, max 600 km).

• Spatial Tracking: Tracks live coordinates of active trains approaching the station.

• Smart Filter: Automatically ignores trains that have already departed or are moving away from the station.

8. Train Speed and Tracking Source (get_train_speed)

Get the current speed and GPS tracking information of an active train.

• Live Speedometer: Returns speed in km/h.

• Locomotive Information: Returns locomotive number and connection status.

• Data Freshness and Source: Exposes data source (GPS vs NTES) and data age.

🛠️ Configuration

The server reads configuration from a .env file at the root.

Create a .env file (copied from .env.example):

CACHE_TIME=60
RAIL_API_BASE_URL=https://api.example.com

• CACHE_TIME: Cache TTL in seconds for API responses.

• RAIL_API_BASE_URL: The base URL for the rail status API source.

🚀 Installation & Build

1. Install Dependencies:

   npm install

2. Build the Server:

   npm run build

3. Running the Server (Locally):

   • Stdio Mode (Standard MCP):

     npx tsx src/server.ts

   • SSE Mode (HTTP Server):

     npx tsx src/http-server.ts

   • Streamable Mode (SSE HTTP Server):

     npx tsx src/http-streamable.ts

🐳 Docker Deployment

The repository includes a multi-stage Dockerfile and .dockerignore for production deployment.

1. Build Docker Image:

   docker build -t railinfo-mcp .

2. Run Container:

   docker run -d --name railinfo-mcp -p 3000:3000 --env-file .env railinfo-mcp

🔌 MCP Client Integration

To integrate this server with Claude Desktop or other MCP clients, add it to your configuration file (e.g., ~/Library/Application Support/Claude/claude_desktop_config.json):

Stdio Transport (Local Node Execution)

{
  "mcpServers": {
    "railinfo-mcp": {
      "command": "node",
      "args": ["/path/to/your/project/railinfo-mcp/dist/server.js"],
      "env": {
        "RAIL_API_BASE_URL": "https://api.example.com",
        "CACHE_TIME": "60"
      }
    }
  }
}

SSE Transport (HTTP Proxy)

If running the server in SSE mode on a VPS under a domain, connect via:

{
  "mcpServers": {
    "railinfo-mcp-sse": {
      "url": "https://your-domain.com/mcp"
    }
  }
}

🗣️ Sample Prompts

Ask your AI assistant questions using the following formats:

Live Train Status & Coordinates

• 📌 "Where is train 12302 today?"

• 📌 "When will train 12357 reach Prayagraj Jn (PRYJ)?"

• 📌 "Get the route map coordinates for train 12951 starting today."

Train Radar & Crossings

• 📌 "What trains are crossing or running ahead of train 12302?"

Timetable & Route Lookups

• 📌 "Show me the full schedule and route timetable of train 12302."

Station boards & Train Search

• 📌 "Show me upcoming trains at New Delhi (NDLS) in the next 2 hours."

• 📌 "Find trains running between Kanpur Central (CNB) and New Delhi (NDLS) starting in the next 4 hours."