azure-resource-health-diagnose

SKILL.md

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:

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:

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

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:

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.)

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:

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

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)

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:

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

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

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:

Immediate Actions (Critical issues):

Emergency fixes to restore service availability

Temporary workarounds to mitigate impact

Escalation procedures for complex issues

Short-term Fixes (High/Medium issues):

Configuration adjustments and resource scaling

Application updates and patches

Monitoring and alerting improvements

Long-term Improvements (All issues):

Architectural changes for better resilience

Preventive measures and monitoring enhancements

Documentation and process improvements

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:

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)

Generate Detailed Report:

# 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)

# Performance and reliability improvements
[Azure CLI commands with explanations]

Phase 3: Long-term Improvements (1-4 weeks)

# 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