Analyze Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations.

Azure Cost Optimize This workflow analyzes Infrastructure as Code (IaC) files and Azure resources to generate cost optimization recommendations. It creates individual GitHub issues for each optimization opportunity plus one EPIC issue to coordinate implementation, enabling efficient tracking and execution of cost savings initiatives. Prerequisites Azure MCP server configured and authenticated GitHub MCP server configured and authenticated Target GitHub repository identified Azure resources deployed (IaC files optional but helpful) Prefer Azure MCP tools ( azmcp ) over direct Azure CLI when available Workflow Steps Step 1: Get Azure Best Practices Action : Retrieve cost optimization best practices before analysis Tools : Azure MCP best practices tool Process : 1. Load Best Practices : Execute azmcp bestpractices get to get some of the latest Azure optimization guidelines. This may not cover all scenarios but provides a foundation. Use these practices to inform subsequent analysis and recommendations as much as possible Reference best practices in optimization recommendations, either from the MCP tool output or general Azure documentation Step 2: Discover Azure Infrastructure Action : Dynamically discover and analyze Azure resources and configurations Tools : Azure MCP tools + Azure CLI fallback + Local file system access Process : 1. Resource Discovery : Execute azmcp subscription list to find available subscriptions Execute azmcp group list subscription <subscription id to find resource groups Get a list of all resources in the relevant group(s): Use az resource list subscription <id resource group <name For each resource type, use MCP tools first if possible, then CLI fallback: azmcp cosmos account list subscription <id Cosmos DB accounts azmcp storage account list subscription <id Storage accounts azmcp monitor workspace list subscription <id Log Analytics workspaces azmcp keyvault key list Key Vaults az webapp list Web Apps (fallback no MCP tool available) az appservice plan list App Service Plans (fallback) az functionapp list Function Apps (fallback) az sql server list SQL Servers (fallback) az redis list Redis Cache (fallback) ... and so on for other resource types 2. IaC Detection : Use file search to scan for IaC files: " / .bicep", " / .tf", " /main.json", " / template .json" Parse resource definitions to understand intended configurations Compare against discovered resources to identify discrepancies Note presence of IaC files for implementation recommendations later on Do NOT use any other file from the repository, only IaC files. Using other files is NOT allowed as it is not a source of truth. If you do not find IaC files, then STOP and report no IaC files found to the user. 3. Configuration Analysis : Extract current SKUs, tiers, and settings for each resource Identify resource relationships and dependencies Map resource utilization patterns where available Step 3: Collect Usage Metrics & Validate Current Costs Action : Gather utilization data AND verify actual resource costs Tools : Azure MCP monitoring tools + Azure CLI Process : 1. Find Monitoring Sources : Use azmcp monitor workspace list subscription <id to find Log Analytics workspaces Use azmcp monitor table list subscription <id workspace <name table type "CustomLog" to discover available data 2. Execute Usage Queries : Use azmcp monitor log query with these predefined queries: Query: "recent" for recent activity patterns Query: "errors" for error level logs indicating issues For custom analysis, use KQL queries: 3. Calculate Baseline Metrics : CPU/Memory utilization averages Database throughput patterns Storage access frequency Function execution rates 4. VALIDATE CURRENT COSTS : Using the SKU/tier configurations discovered in Step 2 Look up current Azure pricing at https://azure.microsoft.com/pricing/ or use az billing commands Document: Resource → Current SKU → Estimated monthly cost Calculate realistic current monthly total before proceeding to recommendations Step 4: Generate Cost Optimization Recommendations Action : Analyze resources to identify optimization opportunities Tools : Local analysis using collected data Process : 1. Apply Optimization Patterns based on resource types found: Compute Optimizations : App Service Plans: Right size based on CPU/memory usage Function Apps: Premium → Consumption plan for low usage Virtual Machines: Scale down oversized instances Database Optimizations : Cosmos DB: Provisioned → Serverless for variable workloads Right size RU/s based on actual usage SQL Database: Right size service tiers based on DTU usage Storage Optimizations : Implement lifecycle policies (Hot → Cool → Archive) Consolidate redundant storage accounts Right size storage tiers based on access patterns Infrastructure Optimizations : Remove unused/redundant resources Implement auto scaling where beneficial Schedule non production environments 2. Calculate Evidence Based Savings : Current validated cost → Target cost = Savings Document pricing source for both current and target configurations 3. Calculate Priority Score for each recommendation: 4. Validate Recommendations : Ensure Azure CLI commands are accurate Verify estimated savings calculations Assess implementation risks and prerequisites Ensure all savings calculations have supporting evidence Step 5: User Confirmation Action : Present summary and get approval before creating GitHub issues Process : 1. Display Optimization Summary : 2. Wait for User Confirmation : Only proceed if user confirms Step 6: Create Individual Optimization Issues Action : Create separate GitHub issues for each optimization opportunity. Label them with "cost optimization" (green color), "azure" (blue color). MCP Tools Required : create issue for each recommendation Process : 1. Create Individual Issues using this template: Title Format : [COST OPT] [Resource Type] [Brief Description] $X/month savings Body Template : bash If IaC files found: Show IaC modifications + deployment File: infrastructure/bicep/modules/app service.bicep Change: sku.name: 'S3' → 'B2' az deployment group create resource group [rg] template file infrastructure/bicep/main.bicep If no IaC files: Direct Azure CLI commands + warning ⚠️ No IaC files found. If they exist elsewhere, modify those instead. az appservice plan update name [plan] sku B2 Step 7: Create EPIC Coordinating Issue Action : Create master issue to track all optimization work. Label it with "cost optimization" (green color), "azure" (blue color), and "epic" (purple color). MCP Tools Required : create issue for EPIC Note about mermaid diagrams : Ensure you verify mermaid syntax is correct and create the diagrams taking accessibility guidelines into account (styling, colors, etc.). Process : 1. Create EPIC Issue : Title : [EPIC] Azure Cost Optimization Initiative $X/month potential savings Body Template : mermaid graph TB subgraph "Resource Group: [name]" [Generated architecture diagram showing current resources and costs] end Error Handling Cost Validation : If savings estimates lack supporting evidence or seem inconsistent with Azure pricing, re verify configurations and pricing sources before proceeding Azure Authentication Failure : Provide manual Azure CLI setup steps No Resources Found : Create informational issue about Azure resource deployment GitHub Creation Failure : Output formatted recommendations to console Insufficient Usage Data : Note limitations and provide configuration based recommendations only Success Criteria ✅ All cost estimates verified against actual resource configurations and Azure pricing ✅ Individual issues created for each optimization (trackable and assignable) ✅ EPIC issue provides comprehensive coordination and tracking ✅ All recommendations include specific, executable Azure CLI commands ✅ Priority scoring enables ROI focused implementation ✅ Architecture diagram accurately represents current state ✅ User confirmation prevents unwanted issue creation

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