AI Suggestion Effectiveness Analyzer MCP Connector for Claude
A+Analyze AI suggestion acceptance, modification, and quality metrics.
This MCP server provides tools to evaluate the effectiveness of AI-driven suggestions in SaaS environments. It calculates key performance indicators including acceptance rates, modification rates, and a composite quality score. Use get_suggestion_acceptance_metrics to get raw performance data, get_quality_score_report to determine overall value, get_type_performance_breakdown to identify high-performing suggestion categories, and get_contextual_efficiency_rating to assess if suggestion timing is optimal.
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