MCP For Claude Marketplace
Retention Risk Scorer

Retention Risk Scorer MCP Connector for Claude

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Predict employee turnover risk and quantify the financial impact of attrition.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner
More Details Connect to Claude

The Retention Risk Scorer is a predictive analytics tool designed to help organizations identify employees at high risk of leaving. By analyzing key human capital metrics such as tenure, promotion history, salary competitiveness, and engagement levels, it provides a normalized turnover risk score (0-100). The server also quantifies the economic burden of vacancies using estimate_replacement_cost and offers actionable management strategies through get_retention_levers. Use calculate_risk_score to evaluate individual profiles and identify primary risk drivers.

turnoverretentionhr-techattritionemployee-engagement

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

calculate_risk_score

Calculate the turnover risk score for an employee

estimate_replacement_cost

Estimate the financial cost of replacing an employee

get_retention_levers

Get recommended management actions to improve retention

See how to talk to your AI agent using Retention Risk Scorer.

Calculate the turnover risk for an employee with 48 months tenure, 24 months since last promotion, a -15% salary gap, and an engagement score of 60 at the Senior level.

The calculated turnover risk score is 72 (High Risk). The primary risk driver identified is the salary gap percentage.

What is the estimated replacement cost for an Executive level employee earning $150,000 per year?

The estimated replacement cost is $375,000. This includes recruitment, onboarding, and lost productivity components.

What actions should I take for an employee with a 85 risk score, -20% salary gap, and 36 months since last promotion?

The prioritized levers are: 1. Review compensation to close the market gap; 2. Evaluate recent performance for potential promotion.

The `calculate_risk_score` tool processes tenure, months since last promotion, salary gap percentage, and engagement scores to generate a normalized value between 0 and 100.

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