# Manage Commvault deployments on Kubernetes using MCP server

The Commvault Kubernetes MCP server lets you manage Commvault deployments on Kubernetes using natural language through AI clients. It translates user requests into Kubernetes and Helm operations, so you can deploy, monitor, and troubleshoot environments without manually running commands. This approach reduces operational complexity and helps you respond quickly to issues, improving availability and cyber resilience across your Kubernetes-based Commvault environment.

To access the Commvault Kubernetes MCP Server repository on GitHub, click here.

## Key features

- Natural language operations: Deploy, scale, upgrade, and troubleshoot Commvault components without manual kubectl or Helm commands.

- End-to-end lifecycle management: Deploy full Commvault rings or individual components.

- Built-in observability: View logs, pod status, and deployment health.

- Multi-user support: Session-scoped namespace isolation for shared environments.

- Automated setup: Guided installation with generated client configurations.

- Flexible deployment: Run locally for testing or in Kubernetes for production use.

## Use cases

Use the MCP server for the following tasks:

- Deploy a complete Commvault environment in Kubernetes.

- Troubleshoot failing pods or misconfigured components.

- Scale infrastructure based on workload demand.

- Upgrade Commvault components across environments.

- Enable self-service operations through AI assistants.

## Architecture and data flow

The MCP server acts as a bridge between AI clients and your Kubernetes cluster:

1. An AI client sends a natural language request.

2. The MCP server converts the request into Kubernetes and Helm operations.

3. The server executes the operations in the cluster

4. Results are returned to the AI client.

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