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MCP Tools Use Cases Overview

This section contains use cases specifically designed for Model Context Protocol (MCP) integration with NetApp ActiveIQ. These use cases focus on metadata management, tagging, and annotation capabilities that enhance the automation and organization of NetApp storage resources.

🎯 Purpose

MCP tools provide a structured way to:

  • Tag and categorize storage objects (SVMs, volumes, LUNs)
  • Attach custom metadata for organizational purposes
  • Search and filter resources based on annotations
  • Automate compliance and governance workflows
  • Enable cost tracking and resource allocation

📚 Available Use Cases

🔧 DevOps Workflows with MCP Integration

Use Case Description Key Features
Backup & Recovery Automated backup and recovery operations MCP-enhanced context, AI-assisted recovery
Capacity Planning Predictive storage capacity management AI forecasting, automated scaling
Event Management Intelligent event processing and response AI correlation, automated workflows
Performance Analysis Real-time performance monitoring and optimization AI-powered insights, predictive analytics
SVM Management Comprehensive Storage Virtual Machine lifecycle Automated provisioning, AI optimization
Volume Operations Advanced volume lifecycle management Intelligent monitoring, automated optimization

🔧 Common MCP Integration Patterns

1. Event-Based Metadata

  • Create events with custom annotations
  • Associate events with storage objects
  • Use event annotations as metadata containers

2. Search and Discovery

  • Query events by annotation content
  • Filter objects by multiple metadata criteria
  • Aggregate results for reporting

3. Automation Workflows

  • Implement tagging standards
  • Automate compliance checking
  • Enable resource lifecycle management

🛠️ Technical Architecture

graph TD
    A[MCP Client] --> B[NetApp ActiveIQ API]
    B --> C[Event Management]
    B --> D[Object Discovery]
    C --> E[Annotation Storage]
    D --> F[Resource Metadata]
    E --> G[Search & Query]
    F --> G
    G --> H[Automation Workflows]
    G --> I[Reporting & Analytics]

🎨 Metadata Schema Examples

Project-Based Tagging

{
  "project": "quarterly-reporting",
  "owner": "finance-team",
  "environment": "production",
  "cost-center": "IT-001"
}

Compliance Tagging

{
  "compliance": "sox",
  "criticality": "high",
  "data-classification": "confidential",
  "retention": "7-years"
}

Team-Based Organization

{
  "team": "storage-admins",
  "contact": "admin@company.com",
  "backup-schedule": "daily",
  "maintenance-window": "sunday-2am"
}

🚀 Getting Started

  1. Choose Your Use Case: Select the most appropriate use case for your needs
  2. Review the Sequence Diagrams: Understand the API interaction patterns
  3. Implement Authentication: Set up proper API credentials
  4. Test with Sample Data: Start with non-production resources
  5. Scale Your Implementation: Apply to production environments

📝 Best Practices

  • Consistent Naming: Use standardized metadata keys and values
  • Error Handling: Implement robust error handling and retry logic
  • Security: Follow authentication and authorization best practices
  • Documentation: Document your metadata schema and conventions
  • Testing: Test thoroughly in non-production environments first