# Chore Fairness Balancer for equitable distribution. AI Agent Connect > Chore Fairness Balancer is a distribution engine that prevents burnout by balancing current workloads against historical contributions. It uses effort points to weight tasks and respects member availability and exclusions. Your AI client can use this to generate optimal schedules, check for logical conflicts in your setup, and predict how changes to chores or availability will impact the overall fairness of the group. ## Overview - **Category:** productivity - **Price:** Free - **Endpoint:** https://edge.vinkius.com/vk_preview_Em817bu2N19Yl69oTlmIyE3gdiQnhoajH3hoGnaD/ai-agent-connect - **Tags:** chore, fairness, scheduling, workload, automation ## Description Managing household or group chores often leads to resentment when one person ends up doing more than their fair share. This MCP solves that by treating labor as a measurable metric. Instead of just listing tasks, it uses effort points to weight each chore based on how much work it actually takes. When you connect this to your AI client, you can stop guessing who should do what. The engine looks at how much work everyone has done in the past to ensure the current week's load is balanced. It also respects specific constraints, like when someone is unavailable or if certain members are excluded from specific tasks. You can run simulations to see if adding a new recurring chore will tip the scales too far for one person, or use it to verify that your chore configuration doesn't have any logical gaps that would leave a task unassigned. It turns chore scheduling from a source of conflict into a data-driven process. ## Tools ### calculate_assignments This tool generates the most balanced chore distribution for a specific timeframe. ### get_member_workload_summary Get a snapshot of how labor is currently distributed across your entire group. ### simulate_fairness_impact Use this to predict how adding new chores or changing member availability will change the fairness score. ### validate_chore_config This tool checks your chore list and member constraints to ensure there are no logical conflicts. ## Prompt Examples **Prompt:** ``` Assign the weekly chores for our household. ``` **Response:** ``` The chores have been assigned: Alice is responsible for Cleaning (5 pts) and Maintenance (3 pts), while Bob is responsible for Logistics (4 pts). The current fairness score is 0.95. ``` **Prompt:** ``` Check if our current chore configuration is valid. ``` **Response:** ``` The configuration is valid. All chores have at least one eligible member based on current availability and exclusions. ``` **Prompt:** ``` How much work is Charlie doing this week? ``` **Response:** ``` Charlie has 8 effort points assigned this period, with a historical effort total of 45 points and a burden ratio of 0.18. ``` ## Capabilities ### Effort-based weighting Your agent assigns points to tasks to reflect actual labor required. ### Historical balancing The MCP adjusts current assignments based on past contributions to prevent burnout. ### Constraint validation Your AI client checks that all chores can be covered by available members. ### Fairness forecasting The tool predicts how schedule changes will affect the group's workload balance. ### Workload snapshots Your agent can pull summaries of how much work each person is carrying. ## Use Cases ### Roommate Chore Rotation Automatically assign cleaning and maintenance tasks so no one roommate carries the load. ### Family Task Management Balance household chores among family members based on their specific availability. ### Co-living Workload Audits Use workload summaries to see if the current chore distribution is actually fair. ### New Task Planning Simulate the impact of adding a new weekly chore before you officially assign it. ## Benefits - Reduces burnout by weighting tasks with effort points. - Prevents resentment by accounting for historical contributions. - Eliminates scheduling conflicts by validating member availability. - Provides data-driven insights into group labor distribution. ## How It Works Connect the MCP to your preferred client and let your agent handle the math. 1. Connect the MCP to Claude, Cursor, or Windsurf via Vinkius. 2. Provide your list of chores and effort points to your agent. 3. Define member availability and any specific exclusions. 4. Ask your agent to calculate the optimal assignments. 5. Review the workload summary or fairness impact to confirm the plan. ## Frequently Asked Questions **How does the MCP decide who does which chore?** It uses effort points to weight the difficulty of tasks and balances these against the historical contributions and current availability of each member. **Can I use this with Claude or Cursor?** Yes, you can connect this MCP to any MCP-compatible client including Claude, Cursor, and Windsurf. **What happens if someone is unavailable for a chore?** The MCP respects member availability and exclusions when calculating assignments to ensure tasks are only given to eligible people. **How do I know if the chore list is missing something?** You can use the validate_chore_config tool to check if your chores and member constraints are logically sound. **Does this MCP host the data?** Vinkius hosts and manages the MCP, so it is ready to use immediately after you connect your client. **How does the system ensure fairness?** The system uses effort points for each task and compares current assignments against the historical effort totals of each member to maintain a stable fairness score. **Can I prevent certain people from doing specific tasks?** Yes, you can define exclusions in the member list to ensure specific members are not assigned tasks they are unable or unwilling to perform. **What happens if a chore cannot be assigned?** If a chore cannot be assigned due to availability or exclusion constraints, it will be listed in the missed constraints section of the assignment result.