# School Run Route Timetable AI Agent Connect > School Run Route Timetable MCP handles the math for school transport logistics. It works backward from a required school arrival time to set departure points, accounts for travel legs, and manages boarding buffers. Your AI client uses this to generate full timetables, check if a route is actually possible, and distribute slack time across stops. ## Overview - **Category:** transportation - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_l9mnRKzWjjyPgN0nXFkO7rhQcdtW6bUgqW54nowX/ai-agent-connect - **Tags:** school, route, timetable, optimization, logistics ## Description Managing school bus routes is a math problem involving tight deadlines and unpredictable variables. This MCP solves that by acting as a specialized scheduling engine. Instead of guessing when a bus should leave the first stop, you give your AI client the arrival deadline and the travel durations between stops. The MCP then calculates the exact departure times needed to hit that window. It doesn't just look at driving time; it factors in the necessary buffers for students to board safely. You can use it to verify if a proposed route is physically possible before you commit to it, or to plan the return trips once the morning drop-offs are finished. It also helps you manage 'slack time' so your drivers aren't constantly rushing or sitting idle, by suggesting how to spread out extra minutes across the route stops. ## Tools ### calculate_departure_schedule This tool builds a complete, ordered timetable of departure times based on a specific sequence of stops and a school arrival deadline. ### get_return_trip_timetable This tool calculates the timing for the vehicle's journey once the school drop-off or arrival is finished. ### optimize_buffer_allocation This tool suggests how to distribute available slack time across different stops to create a more comfortable schedule. ### validate_route_feasibility This tool checks if a proposed sequence of stops and travel durations can actually be completed within a specific timeframe. ## Prompt Examples **Prompt:** ``` Generate a timetable for stops at 123 Maple St and 456 Oak St, arriving at school by 08:30. Leg durations are 10 and 15 minutes, and each stop has a 5 minute buffer. ``` **Response:** ``` The departure times are: 123 Maple St at 07:50 and 456 Oak St at 08:05. ``` **Prompt:** ``` Is it feasible to visit 3 stops with 10 minute legs and 5 minute buffers, arriving at school at 08:00, if the earliest departure is 07:00? ``` **Response:** ``` Yes, the route is feasible with a total duration of 45 minutes. ``` **Prompt:** ``` Calculate the return trip starting from school at 15:00, with a 20 minute leg to the first stop and a 10 minute leg to the depot. ``` **Response:** ``` The return schedule is: First stop at 15:20 and Depot at 15:30. ``` ## Capabilities ### Backward Scheduling Your agent calculates departure times by working backward from a fixed school arrival deadline. ### Route Validation Your AI client checks if a sequence of stops is physically possible within your time constraints. ### Return Trip Planning Your agent generates timing for the vehicle's journey after the initial drop-off is complete. ### Buffer Optimization Your AI client distributes extra time across stops to prevent a rushed schedule. ## Use Cases ### Morning Route Planning Create a precise departure list for a bus that must arrive at a school by a specific time. ### Route Feasibility Checks Confirm if a new set of student stops can be serviced within the available morning window. ### Afternoon Return Trips Plan the vehicle's movement from the school back to the depot or the next stop. ### Schedule Smoothing Use buffer allocation to ensure drivers have enough time at each stop to avoid running late. ## Benefits - Calculates departure times based on arrival deadlines rather than just driving time. - Identifies impossible routes before they are deployed. - Distributes slack time to prevent driver fatigue and delays. - Automates the creation of return trip schedules. ## How It Works You connect the MCP to your AI client and start providing route data. 1. Connect your preferred MCP-compatible client to Vinkius. 2. Provide your stop sequence, travel durations, and arrival deadlines to your AI agent. 3. The agent uses the MCP tools to run the scheduling math. 4. Receive a completed timetable or feasibility report directly in your chat interface. ## Frequently Asked Questions **How does this MCP handle boarding time?** The MCP accounts for mandatory boarding buffers when calculating departure times to ensure the vehicle stays on schedule. **Can I use this with Claude or Cursor?** Yes, you can connect this MCP to any MCP-compatible client like Claude, Cursor, or Windsurf via Vinkius. **Does it only work for morning routes?** No, it includes specific tools for calculating return trip timetables after the school arrival is complete. **How does it decide when a route is impossible?** The validation tool compares the total travel time and required buffers against your available time window to see if the sequence can be completed. **What is buffer allocation?** It is a process where the MCP suggests how to spread out extra time across different stops to create a more reliable and less rushed schedule. **How do I generate a full timetable?** You can use the `calculate_departure_schedule` tool by providing the pickup addresses, the school arrival deadline, leg durations, and stop buffers. **Can I check if my route is physically possible?** Yes, the `validate_route_feasibility` tool checks if the sequence of stops and durations fits within your required timeframe. **How can I add extra safety time to my stops?** Use the `optimize_buffer_allocation` tool to distribute available slack time across your stops using either an even or front-loaded distribution.