MCP For Claude Marketplace
Kubernetes HPA Scaling Simulator

Kubernetes HPA Scaling Simulator MCP Connector for Claude

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Simulate Kubernetes Horizontal Pod Autoscaler behavior and stability.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner
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This MCP server provides a high-fidelity simulation engine for Kubernetes Horizontal Pod Autoscaler (HPA) behavior. It allows you to project how replica counts will evolve over time based on varying load patterns, applying standard HPA scaling logic and stabilization windows. Use calculate_target_replicas to find the ideal pod count for a specific metric, simulate_scaling_timeline to model long-term scaling trends, and identify_thrashing_patterns to detect potential oscillation risks in your configuration. It also includes validate_hpa_config to ensure your parameters like min/max replicas and cooldown windows are logically sound.

hpakubernetesautoscalingsimulationdevopsinfrastructure

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

calculate_target_replicas

Calculates the target number of replicas based on current metric and utilization

identify_thrashing_patterns

Analyzes a scaling timeline to detect frequent oscillations (thrashing)

simulate_scaling_timeline

Simulates how the replica count evolves over a series of metric observations

validate_hpa_config

Validates the HPA configuration parameters

See how to talk to your AI agent using Kubernetes HPA Scaling Simulator.

Calculate the target replicas if I have 5 pods running, current CPU is 80%, and my target is 50%.

Based on a current usage of 80% against a 50% target, the required number of replicas is 8.

Will my HPA config with min 2, max 10, and a 300s window be valid?

Yes, the configuration is valid. The minimum replicas do not exceed the maximum, and the stabilization window is non-negative.

Analyze this timeline for thrashing: [{'timestamp': '2024-01-01T00:00:00Z', 'observedMetric': 50, 'activeReplicaCount': 2}, {'timestamp': '2024-01-01T00:05:00Z', 'observedMetric': 90, 'activeReplicaCount': 3}, {'timestamp': '2024-01-01T00:10:00Z', 'observedMetric': 40, 'activeReplicaCount': 2}]

The scaling timeline shows 1 oscillation event. The risk level is low as it does not exceed the threshold.

It uses the standard HPA formula: (current metric / target utilization) * current replicas, then rounds up and clamps within your min/max bounds.

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