Azure Resource Health Diagnose

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 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

1. 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.)

1. 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

1. Execute Diagnostic Queries:

Use azmcp-monitor-log-query with targeted KQL queries based on resource type:

General Error Analysis: ``kql // 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: ``kql // 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: ```kql // 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 actionnames == "CONNECTION_FAILED" | summarize ConnectionFailures=count() by bin(TimeGenerated, 1h) ```

1. 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

1. Root Cause Analysis:

• Configuration Issues: Incorrect settings, missing dependencies
• Resource Constraints: CPU/memory/disk limitations, throttling
• Network Issues: Connectivity problems, DNS resolution, firewall rules
• Application Issues: Code bugs, memory leaks, inefficient queries
• External Dependencies: Third-party service failures, API limits
• Security Issues: Authentication failures, certificate expiration

1. Impact Assessment:

• Determine business impact and affected users/systems
• Evaluate data integrity and security implications
• Assess recovery time objectives and priorities

Step 6: Generate Remediation Plan

Action: Create a comprehensive plan to address identified issues Process:

1. Immediate Actions (Critical issues):

• Emergency fixes to restore service availability
• Temporary workarounds to mitigate impact
• Escalation procedures for complex issues

1. Short-term Fixes (High/Medium issues):

• Configuration adjustments and resource scaling
• Application updates and patches
• Monitoring and alerting improvements

1. Long-term Improvements (All issues):

• Architectural changes for better resilience
• Preventive measures and monitoring enhancements
• Documentation and process improvements

1. Implementation Steps:

• Prioritized action items with specific Azure CLI commands
• Testing and validation procedures
• Rollback plans for each change
• Monitoring to verify issue resolution

Step 7: User Confirmation & Report Generation

Action: Present findings and get approval for remediation actions Process:

1. Display Health Assessment Summary:

``` 🏥 Azure Resource Health Assessment

📊 Resource Overview: • Resource: [Name] ([Type]) • Status: [Healthy/Warning/Critical] • Location: [Region] • Last Analyzed: [Timestamp]

🚨 Issues Identified: • Critical: X issues requiring immediate attention • High: Y issues affecting performance/reliability • Medium: Z issues for optimization • Low: N informational items

🔍 Top Issues:

1. [Issue Type]: [Description] - Impact: [High/Medium/Low]
2. [Issue Type]: [Description] - Impact: [High/Medium/Low]
3. [Issue Type]: [Description] - Impact: [High/Medium/Low]

🛠️ Remediation Plan: • Immediate Actions: X items • Short-term Fixes: Y items • Long-term Improvements: Z items • Estimated Resolution Time: [Timeline]

❓ Proceed with detailed remediation plan? (y/n) ```

1. Generate Detailed Report:

```markdown # Azure Resource Health Report: [Resource Name]

Generated: [Timestamp] Resource: [Full Resource ID] Overall Health: [Status with color indicator]

## 🔍 Executive Summary [Brief overview of health status and key findings]

## 📊 Health Metrics

• Availability: X% over last 24h
• Performance: [Average response time/throughput]
• Error Rate: X% over last 24h
• Resource Utilization: [CPU/Memory/Storage percentages]

## 🚨 Issues Identified

### Critical Issues

• [Issue 1]: [Description]
• Root Cause: [Analysis]
• Impact: [Business impact]
• Immediate Action: [Required steps]

### High Priority Issues

• [Issue 2]: [Description]
• Root Cause: [Analysis]
• Impact: [Performance/reliability impact]
• Recommended Fix: [Solution steps]

## 🛠️ Remediation Plan

### Phase 1: Immediate Actions (0-2 hours) ``bash # Critical fixes to restore service [Azure CLI commands with explanations] ``

### Phase 2: Short-term Fixes (2-24 hours) ``bash # Performance and reliability improvements [Azure CLI commands with explanations] ``

### Phase 3: Long-term Improvements (1-4 weeks) ``bash # Architectural and preventive measures [Azure CLI commands and configuration changes] ``

## 📈 Monitoring Recommendations

• Alerts to Configure: [List of recommended alerts]
• Dashboards to Create: [Monitoring dashboard suggestions]
• Regular Health Checks: [Recommended frequency and scope]

## ✅ Validation Steps

• [ ] Verify issue resolution through logs
• [ ] Confirm performance improvements
• [ ] Test application functionality
• [ ] Update monitoring and alerting
• [ ] Document lessons learned

## 📝 Prevention Measures

• [Recommendations to prevent similar issues]
• [Process improvements]
• [Monitoring enhancements]

```

Error Handling

• Resource Not Found: Provide guidance on resource name/location specification
• Authentication Issues: Guide user through Azure authentication setup
• Insufficient Permissions: List required RBAC roles for resource access
• No Logs Available: Suggest enabling diagnostic settings and waiting for data
• Query Timeouts: Break down analysis into smaller time windows
• Service-Specific Issues: Provide generic health assessment with limitations noted

Success Criteria

• ✅ Resource health status accurately assessed
• ✅ All significant issues identified and categorized
• ✅ Root cause analysis completed for major problems
• ✅ Actionable remediation plan with specific steps provided
• ✅ Monitoring and prevention recommendations included
• ✅ Clear prioritization of issues by business impact
• ✅ Implementation steps include validation and rollback procedures

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: github
• Source: github/awesome-copilot
• License: MIT
• Homepage: https://awesome-copilot.github.com/

Install and usage instructions live in the source repository linked above.