MCP For Claude Marketplace
AI Feature Error Metrics Engine

AI Feature Error Metrics Engine MCP Connector for Claude

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Quantify AI feature failures and prioritize reliability engineering efforts.

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

This MCP server provides a specialized metrics engine for SaaS providers to assess the health of AI features. It calculates critical metrics such as error rates, user impact scores, and reliability improvement priorities. By using tools like calculate_error_metrics and analyze_reliability_priorities, engineering teams can move beyond raw error counts to understand the actual friction experienced by users and strategically rank which error types, such as Hallucinations or Model Timeouts, require immediate attention.

ai-metricserror-analysissaasreliabilityobservability

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

analyze_reliability_priorities

Generates a strategic list of which error types to fix first

calculate_error_metrics

Provides the fundamental error rate and user impact assessment

get_user_experience_health

Translates technical metrics into a qualitative assessment for product stakeholders

summarize_error_distribution

Breaks down how errors are distributed across different categories

See how to talk to your AI agent using AI Feature Error Metrics Engine.

Calculate the error metrics for 50 errors per 1000 requests, where 40% are user-visible and the recovery rate is 50%.

The calculated error rate is 5.0%, the user impact score is 1.0, and the effective error rate is 1.0%.

What is the current user experience health if the user impact score is 85?

The health status is Critical. The user experience is experiencing significant friction that requires immediate attention.

Analyze reliability priorities for these error types: Hallucination (weight 1.5) and Timeout (weight 1.0) given the current metrics.

The highest priority is Hallucination with a score of 1.5, followed by Timeout with a score of 1.0.

The `calculate_error_metrics` tool calculates impact by weighing the error rate against user visibility and the system's ability to recover via automatic retries.

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