MCP For Claude Marketplace
AI Output Quality Metrics Engine

AI Output Quality Metrics Engine MCP Connector for Claude

A+

Quantifies AI performance using feedback, regeneration rates, and acceptance metrics.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a standardized framework for measuring the excellence of AI-generated content. By synthesizing qualitative user feedback with quantitative behavioral signals, it calculates a precise quality score (0-100). Use get_quality_score to evaluate specific models, get_quality_trend to monitor performance evolution, get_satisfaction_correlation to check alignment between user sentiment and actual usage, and get_use_case_benchmarks to compare results against task-specific standards.

metricsquality-assuranceai-performanceuser-feedbackmodel-evaluation

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

get_quality_score

Calculates the primary quality metric for a specific AI model or version

get_quality_trend

Analyzes how quality metrics have evolved over a specific period

get_satisfaction_correlation

Determines if user feedback (explicit) aligns with usage behavior (implicit)

get_use_case_benchmarks

Retrieves standard quality thresholds for different types of AI tasks

See how to talk to your AI agent using AI Output Quality Metrics Engine.

What is the current quality score for model 'gpt-4o' in the 'coding' use case?

The quality score for 'gpt-4o' in the 'coding' use case is 88.5, with a high confidence interval.

Is the quality of my model improving over the last 30 days?

Yes, the quality trend shows a positive velocity with a steady increase in the score over the last 30 days.

Show me the benchmarks for high-precision tasks.

For high-precision tasks, the target score is 95.0, the ideal acceptance rate is 92.0%, and the acceptable edit threshold is 0.5.

The score is a weighted synthesis of the acceptance rate and feedback ratio, with penalties applied for high regeneration rates and edit counts via `get_quality_score`.

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