bus-scheduling-mcp

bus-scheduling-mcp

Enables LLM clients to access scheduling data, KPIs, routes, and trigger optimization algorithms via MCP.

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README

Bus Scheduling MCP Server

This is a Model Context Protocol (MCP) server designed to interface with the Bus Scheduling Backend system. It allows MCP-compatible LLM clients (like Claude Desktop, Cursor, Cline, etc.) to access scheduling data, read KPIs, list routes, and trigger/monitor optimization algorithms.

Prerequisites

  • Node.js (v18 or higher recommended)
  • The accompanying Python backend must be running (by default on http://127.0.0.1:8000).

Installation

  1. Install dependencies:
    npm install
    
  2. Build the TypeScript code:
    npm run build
    

Configuration

If your backend is running on a different URL, you can configure it via a .env file in the root of this /mcp-server directory.

Example .env:

API_BASE_URL=http://localhost:8000/api

Running the Server

For standard usage as an MCP Server:

Point your MCP client configuration (e.g. cline_mcp_settings.json or equivalent) to the built index.js file.

Example Cursor/Claude config:

{
  "mcpServers": {
    "bus-scheduling": {
      "command": "node",
      "args": ["/absolute/path/to/mcp-server/build/index.js"]
    }
  }
}

For development:

Run the development watcher (useful if you are actively modifying the MCP server code):

npm run dev

Available Tools

The server exposes the following tools directly to the AI:

  • Dashboard & Metrics
    • scheduling_get_kpi: Fetch KPIs (vehicle status, departures, punctuality).
    • scheduling_get_trends: Get passenger flow trends.
  • Routes & Data
    • scheduling_list_routes: List active bus routes.
    • scheduling_get_route_details: Fetch stations and departures for a specific route.
  • Plans & Gantt
    • scheduling_list_plans: List all saved scheduling plans.
    • scheduling_get_gantt_data: Get task formats for Gantt charts.
  • Optimization
    • scheduling_get_optimization_algorithms: List available optimization scripts.
    • scheduling_run_optimization: Trigger an algorithm task.
    • scheduling_get_optimization_status: Check the status of a running algorithm task.

Testing & Evaluations

You can inspect the capabilities of this server directly using the official MCP inspector:

npx @modelcontextprotocol/inspector node build/index.js

10 comprehensive use cases and checks are available in evaluations.xml in this project's root folder.

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