Agent SLA Compliance Monitor

Agent SLA Compliance Monitor MCP Connector for Claude

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Deterministic engine for calculating real-time SLA compliance, error budget depletion, and health scoring.

3 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a deterministic engine for monitoring autonomous agent reliability. It evaluates performance against predefined Service Level Agreements (SLAs) by calculating core metrics like latency, availability, and accuracy. Use calculate_compliance_metrics to evaluate how well the system meets targets, analyze_error_budget to track budget depletion and burn rates, and get_composite_health_score to generate a unified health metric using a geometric mean. It is designed to identify critical violations and forecast error budget exhaustion.

slacomplianceerror-budgetagent-monitoringreliability-engineering

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

get_composite_health_score

Generates a single unified score representing the total system health

analyze_error_budget

Determines the status of the error budget, including remaining capacity and burn rate

calculate_compliance_metrics

Calculates core compliance percentages for latency, availability, and accuracy

See how to talk to your AI agent using Agent SLA Compliance Monitor.

Calculate the compliance metrics for a system with a 99.9% availability target and 500ms P99 latency target, given these measurements: [{"responseTimeMs": 450, "isCorrect": true, "timestamp": 1700000000}, {"responseTimeMs": 550, "isCorrect": false, "timestamp": 1700000060}].

The response time compliance is 50.0%, the availability compliance is 100.0%, and the accuracy compliance is 50.0%.

Analyze the error budget if availability compliance is 99.5%, the target is 99.9%, and the error budget is 0.1%.

The error budget remaining is 0.05%, and the burn rate is 1.25.

What is the composite health score for compliance values of 99.5, 99.9, and 98.0?

The composite SLA score is 99.13.

The score is calculated as the geometric mean of all individual compliance percentages, such as those provided by `get_composite_health_score`.

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