Personal Record Tracker

Personal Record Tracker MCP Connector for Claude

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Predictive analytics for athletes to track PRs and forecast performance peaks.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides predictive analytics for athletes to track historical personal records (PRs) and forecast future performance peaks. By analyzing historical data and current training states, it calculates progression rates and target values. Use get_progression_metrics to understand improvement velocity, project_next_targets to set realistic goals for upcoming cycles, and calculate_achievement_probability to assess the likelihood of hitting specific targets. It also provides phase-specific insights via get_training_phase_impact to adjust expectations based on current training intensity.

athleteperformancepredictiontrainingfitness-tracking

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

calculate_achievement_probability

Estimate the probability of hitting a specific target value

get_progression_metrics

Each entry must be a JSON string representing an object with "value" (number) and "date" (ISO string). Calculate athlete progression metrics from historical personal records

get_training_phase_impact

Retrieve the performance multiplier and volatility for a training phase

project_next_targets

Project future personal record targets based on training phase and timeline

See how to talk to your AI agent using Personal Record Tracker.

How fast am I improving based on my last 5 PRs?

Your average progression rate is 2.5kg per month with a stable trend direction.

What is my target squat for 30 days from now if I am in the Intensification phase?

Your projected target squat for 30 days from now is 145kg, with a confidence interval of +/- 2kg.

What is the probability I will hit a 200kg deadlift in the next 60 days?

There is a 65% probability of hitting 200kg within the next 60 days given your current trajectory.

Projections are based on your historical progression rate and current training phase. While they provide statistical estimates, actual performance depends on physiological factors and training adherence.

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