# Seasonal Routine Switcher AI Agent Connect > Seasonal Routine Switcher adjusts your daily time blocks by accounting for shifting daylight, weather delays, and seasonal commitments. It gives your AI client the environmental context needed to rebuild your routine when the seasons change, ensuring your schedule stays realistic regardless of the weather or sun cycles. ## Overview - **Category:** productivity - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_KUOvihZ9FqWc1HtBm93dsnAHv7rnW0Ao9kGrkpWq/ai-agent-connect - **Tags:** routine, seasonal, schedule, weather, daylight ## Description Your daily routine shouldn't stay static when the days get shorter or the weather gets worse. This MCP gives your AI client the ability to factor in environmental changes that usually break a rigid schedule. Instead of fighting against a winter commute or trying to squeeze outdoor tasks into dark evenings, you can let your agent recalculate your time blocks. It works by pulling regional metadata like sunrise and sunset times or weather impact factors. You can use these inputs to shift work windows, adjust commute buffers for snow, or move physical activities to better daylight hours. It's about making sure your plan actually works in the real world, not just on a digital calendar. Whether you're dealing with heavy snow delays or just the loss of afternoon light, this MCP helps your agent build a schedule that respects the reality of your environment. ## Tools ### calculate_seasonal_adjustment This tool computes new time blocks after applying seasonal factors like daylight or weather. It handles the math for shifting your schedule. ### get_routine_template This tool retrieves a baseline daily routine structure. It provides the starting point for any seasonal adjustments. ### get_seasonal_metadata This tool provides available seasonal constants for a specific region. Use it to get daylight hours and weather profiles. ### validate_routine_feasibility This tool checks if an adjusted routine is logically sound. It ensures your new schedule doesn't have overlapping tasks or impossible timing. ## Prompt Examples **Prompt:** ``` Adjust my standard weekday routine for winter in New York. Sunrise is at 07:15, sunset is at 16:45, and I need a 20-minute extra commute buffer due to snow. The weather impact factor is 1.2. ``` **Response:** ``` Your adjusted winter routine for New York is ready. Your commute has been extended, and outdoor activities have been shifted to the 07:15 - 16:45 daylight window. ``` **Prompt:** ``` What are the seasonal weather profiles and daylight hours for region US-NY? ``` **Response:** ``` In US-NY, the average daylight hours vary significantly by season, and characteristic weather profiles include snow, rain, and high humidity. ``` **Prompt:** ``` Is my adjusted routine feasible if I need at least 8 hours of sleep? ``` **Response:** ``` Yes, your routine is feasible and provides the required 8 hours of sleep without any overlapping time blocks. ``` ## Capabilities ### Daylight-Aware Scheduling Your agent uses sunrise and sunset data to move outdoor tasks into daylight hours. ### Weather-Based Buffers The AI adds extra time to commutes or tasks when weather impact factors are high. ### Routine Validation Your agent checks for logical errors or overlapping blocks in a new seasonal schedule. ### Regional Data Retrieval The agent pulls specific environmental constants for your current location. ## Use Cases ### Winter Commute Planning Your agent adds extra time to your morning travel when snow or ice is reported. ### Daylight Optimization The AI moves exercise or errands to the specific hours when the sun is up. ### Seasonal Routine Rebuilding You trigger a full schedule recalculation when moving from summer to autumn. ### Feasibility Auditing The agent verifies that a new, weather-adjusted schedule still allows for enough sleep. ## Benefits - Accounts for daylight shifts to keep outdoor tasks viable. - Adds commute buffers based on weather impact factors. - Prevents scheduling conflicts through feasibility checks. - Uses regional metadata to automate environmental adjustments. ## How It Works Connect the MCP to your client and let your agent handle the seasonal math. 1. Connect the MCP to your preferred AI client via Vinkius. 2. Provide your agent with your current routine or a template. 3. The agent pulls regional weather and daylight metadata. 4. The agent calculates new time blocks based on these environmental factors. 5. The agent validates the new schedule to ensure it is logically sound. ## Frequently Asked Questions **How does the MCP handle weather changes?** It uses weather impact factors to adjust time blocks, such as adding buffer time for commutes during snow. **Can I use this with Claude or Cursor?** Yes, this MCP works with any MCP-compatible client including Claude, Cursor, and Windsurf. **Does it work for any location?** It works for any region where the agent can retrieve seasonal metadata and daylight constants. **How does it ensure my schedule is actually possible?** The agent uses a validation tool to check for overlapping blocks and ensures requirements like sleep hours are met. **Do I need to host this myself?** No, Vinkius hosts and manages the MCP, so you just connect and start using it. **How does the tool handle weather changes?** You can use the `calculate_seasonal_adjustment` tool with a `weatherImpactFactor` to increase or decrease the duration of activities based on environmental conditions. **Can I check if my new schedule is actually possible?** Yes, the `validate_routine_feasibility` tool checks if your adjusted routine fits within a 24-hour window and meets your minimum sleep requirements. **How do I find daylight information for my area?** Use the `get_seasonal_metadata` tool with your specific region code to retrieve average daylight hours and seasonal weather profiles.