/azure-resource-health-diagnose

name: azure-resource-health-diagnose
description: 'Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems.'

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**:

   // Application Insights - Failed requests
   requests
   | where timestamp > ago(24h)
   | where success == false
   | summarize FailureCount=count() by resultCode, bin(timestamp, 1h)
   | order by timestamp desc
   
   // Database - Connection failures
   AzureDiagnostics
   | where ResourceProvider == "MICROSOFT.SQL"
   | where Category == "SQLSecurityAuditEvents"
   | where action_name_s == "CONNECTION_FAILED"
   | summarize ConnectionFailures=count() by bin(TimeGenerated, 1h)

3. **Pattern Recognition**:

• Identify recurring error patterns or anomalies
• Correlate errors with deployment times or configuration changes
• Analyze performance trends and degradation patterns
• Look for dependency failures or external service issues

Step 5: Issue Classification & Root Cause Analysis

**Action**: Categorize identified issues and determine root causes **Process**: 1. **Issue Classification**:

• **Critical**: Service unavailable, data loss, security breaches
• **High**: Performance degradation, intermittent failures, high error rates
• **Medium**: Warnings, suboptimal configuration, minor performance issues
• **Low**: Informational alerts, optimization opportunities

2. **Root Cause Analysis**:

• **Configuration Issues**: Incorrect settings, missing dependencies
• **Resource C