Body Drag Distance Estimator

Body Drag Distance Estimator MCP Connector for Claude

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Predicts drift distance and recovery time for kiteboarders using environmental vector analysis.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides critical vector analysis for kiteboarders who have lost control of their equipment. By calculating the interaction between wind, current, and kite force, it helps predict where a person has drifted. Use get_drift_velocity to find the instantaneous speed of movement, predict_drift_distance to estimate total travel distance over time, and estimate_recovery_time to determine how long it will take to reach the target area. It also includes calculate_optimal_search_pattern to suggest the most efficient visual search method like zig-zag or circular patterns based on environmental conditions.

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4 tools expose this connector's capabilities to your AI agent.

calculate_optimal_search_pattern

Determines the most effective visual search pattern based on the predicted drift area

estimate_recovery_time

Estimates how long it will take to reach the board or cover the drift zone

get_drift_velocity

Calculates the instantaneous speed at which a person is being moved by environmental forces

predict_drift_distance

Predicts the total distance traveled from the starting point over a specific duration

See how to talk to your AI agent using Body Drag Distance Estimator.

Calculate the drift velocity if the kite power is 500N, wave direction is 90 degrees, wind speed is 10m/s, and current speed is 0.5m/s.

The calculated drift velocity is 12.4 meters per minute with a drift direction of 85 degrees.

If a person has drifted at 5 meters per minute for 30 minutes with a fatigue level of 1.2, what is the total distance traveled?

The total distance traveled is 180 meters.

How long will it take to recover if the drift distance is 500 meters, the velocity is 10 meters per minute, and the fatigue level is 1.5?

It will take 75 minutes to reach the board.

The `estimate_recovery_time` and `predict_drift_distance` tools use a fatigue coefficient to adjust the effective drift distance and velocity, simulating how a person's ability to resist drift decreases over time.

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