AI Suggestion Effectiveness Analyzer

AI Suggestion Effectiveness Analyzer MCP Connector for Claude

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Analyze AI suggestion acceptance, modification, and quality metrics.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

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.

metricssaasai-performanceuser-interactionquality-scoring

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

get_suggestion_acceptance_metrics

Calculates raw performance metrics for AI suggestions

get_type_performance_breakdown

get_contextual_efficiency_rating

Evaluates the efficiency of suggestion timing relative to user action

get_quality_score_report

Calculates the overall quality score and status of AI suggestions

See how to talk to your AI agent using AI Suggestion Effectiveness Analyzer.

What is the raw performance of my AI suggestions if 100 were shown, 40 were accepted, and 20 were modified?

The acceptance rate is 40%, the modification rate is 20%, and the total interaction rate is 60%.

How is the quality score for these metrics: acceptance rate 0.5, modification rate 0.2, and they were proactive?

The quality score is 0.85 and the status is Excellent.

Is the timing of a suggestion efficient if it appeared 2 seconds after an action and was accepted?

The efficiency rating is 0.95.

The quality score combines acceptance and modification rates, giving higher weight to unmodified acceptances. It can also be adjusted based on whether suggestions were proactive.

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