Azure Resource Health & Issue Diagnosis

This workflow analyzes a specific Azure resource to assess its health status, diagnose potential issues using logs and telemetry data, and develop a comprehensive remediation plan for any problems discovered.

Prerequisites

• Azure MCP server configured and authenticated
• Target Azure resource identified (name and optionally resource group/subscription)
• Resource must be deployed and running to generate logs/telemetry
• Prefer Azure MCP tools (azmcp-*) over direct Azure CLI when available

Workflow Steps

Step 1: Get Azure Best Practices

Action: Retrieve diagnostic and troubleshooting best practices Tools: Azure MCP best practices tool Process:

1. Load Best Practices:
   • Execute Azure best practices tool to get diagnostic guidelines
   • Focus on health monitoring, log analysis, and issue resolution patterns
   • Use these practices to inform diagnostic approach and remediation recommendations

Step 2: Resource Discovery & Identification

Action: Locate and identify the target Azure resource Tools: Azure MCP tools + Azure CLI fallback Process:

1. Resource Lookup:

   • If only resource name provided: Search across subscriptions using azmcp-subscription-list
   • Use az resource list --name <resource-name> to find matching resources
   • If multiple matches found, prompt user to specify subscription/resource group
   • Gather detailed resource information:
     • Resource type and current status
     • Location, tags, and configuration
     • Associated services and dependencies

2. Resource Type Detection:

   • Identify resource type to determine appropriate diagnostic approach:
     • Web Apps/Function Apps: Application logs, performance metrics, dependency tracking
     • Virtual Machines: System logs, performance counters, boot diagnostics
     • Cosmos DB: Request metrics, throttling, partition statistics
     • Storage Accounts: Access logs, performance metrics, availability
     • SQL Database: Query performance, connection logs, resource utilization
     • Application Insights: Application telemetry, exceptions, dependencies
     • Key Vault: Access logs, certificate status, secret usage
     • Service Bus: Message metrics, dead letter queues, throughput

Step 3: Health Status Assessment

Action: Evaluate current resource health and availability Tools: Azure MCP monitoring tools + Azure CLI Process:

1. Basic Health Check:

   • Check resource provisioning state and operational status
   • Verify service availability and responsiveness
   • Review recent deployment or configuration changes
   • Assess current resource utilization (CPU, memory, storage, etc.)

2. Service-Specific Health Indicators:

   • Web Apps: HTTP response codes, response times, uptime
   • Databases: Connection success rate, query performance, deadlocks
   • Storage: Availability percentage, request success rate, latency
   • VMs: Boot diagnostics, guest OS metrics, network connectivity
   • Functions: Execution success rate, duration, error frequency

Step 4: Log & Telemetry Analysis

Action: Analyze logs and telemetry to identify issues and patterns Tools: Azure MCP monitoring tools for Log Analytics queries Process:

1. Find Monitoring Sources:

   • Use azmcp-monitor-workspace-list to identify Log Analytics workspaces
   • Locate Application Insights instances associated with the resource
   • Identify relevant log tables using azmcp-monitor-table-list

2. Execute Diagnostic Queries: Use azmcp-monitor-log-query with targeted KQL queries based on resource type:

   General Error Analysis:

   // Recent errors and exceptions
   union isfuzzy=true 
       AzureDiagnostics,
       AppServiceHTTPLogs,
       AppServiceAppLogs,
       AzureActivity
   | where TimeGenerated > ago(24h)
   | where Level == "Error" or ResultType != "Success"
   | summarize ErrorCount=count() by Resource, ResultType, bin(TimeGenerated, 1h)
   | order by TimeGenerated desc
   

   Performance Analysis:

   // Performance degradation patterns
   Perf
   | where TimeGenerated > ago(7d)
   | where ObjectName == "Processor" and CounterName == "% Processor Time"
   | summarize avg(CounterValue) by Computer, bin(TimeGenerated, 1h)
   | where avg_CounterValue > 80
   

   Application-Specific Queries:

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