cloud▌

Category: test

Complete Cloudflare platform integration with decision trees for compute, storage, AI, networking, security, and infrastructure-as-code. \n \n Covers 50+ Cloudflare products organized by use case: serverless functions (Workers, Pages), data storage (KV, D1, R2), AI/ML (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), and security (WAF, DDoS, Bot Management) \n Includes decision trees to route tasks to the right product based on requirements (e.g., \"run code\" → Workers vs. Pag

Reusable Terraform modules for AWS, Azure, GCP, and OCI infrastructure with standardized patterns and best practices. \n \n Provides pre-built module templates across four cloud providers covering core services like VPC/VNet, Kubernetes clusters, databases, and object storage \n Enforces consistent module structure with input variables, outputs, documentation, examples, and Terratest-based testing \n Includes validation blocks, conditional resources via count/for_each, and tagging strategies for

Check and manage Azure quotas and usage across regions for deployment planning and capacity validation. \n \n Azure CLI ( az quota ) is the only reliable method for checking quotas; REST API and Portal are unreliable and may show misleading \"No Limit\" values that do not indicate unlimited capacity \n Supports four core workflows: checking specific resource quotas, comparing capacity across regions, requesting quota increases, and listing all quotas for a provider \n Quota resource names do not

Recommend Azure VM sizes, scale sets, and configurations based on workload type, performance needs, and budget. \n \n Analyzes workload requirements (web, database, ML, batch, HPC) and recommends appropriate VM families, sizes, and instance counts without requiring an Azure subscription \n Supports both single VMs and VM Scale Sets (VMSS) with guidance on autoscaling, load balancing, and orchestration modes (Flexible vs Uniform) \n Queries the unauthenticated Azure Retail Prices API to provide c

Fast discovery and inventory of Azure resources across subscriptions using Resource Graph queries. \n \n Queries any Azure resource type (VMs, storage accounts, web apps, container apps, Key Vaults, etc.) across subscriptions and resource groups using KQL \n Identifies orphaned resources, missing tags, and resources in specific states without needing dedicated MCP tools \n Routes single-resource-type queries to specialized MCP tools (compute, storage, cosmos, acr, aks) when available; falls back

Validates Bicep deployments to Azure before execution, detecting syntax errors, permission issues, and previewing infrastructure changes. \n \n Supports both Azure CLI ( az ) and Azure Developer CLI ( azd ) workflows, auto-detecting project type via azure.yaml \n Runs Bicep syntax validation, what-if analysis across all deployment scopes (resource group, subscription, management group, tenant), and permission checks with fallback to reduced-permission validation \n Captures detailed what-if resu

Analyze Azure resource groups and generate detailed Mermaid diagrams visualizing resource relationships and architecture. \n \n Discovers all resources within a resource group and maps their interdependencies, including network connections, data flows, identity bindings, and configuration references \n Generates layered Mermaid diagrams with logical grouping by function (Network, Compute, Data, Security, Monitoring) and includes SKU, tier, and configuration details in node labels \n Creates comp

Analyze Azure resource groups and generate detailed Mermaid diagrams showing resource relationships and architecture. \n \n Discovers and analyzes all resources within a specified resource group, capturing configurations, SKUs, network settings, and identity details \n Maps relationships between resources including network connections, data flows, identity bindings, and dependencies \n Generates layered Mermaid diagrams with subgraphs organized by function (Network, Compute, Data, Security, Moni

Query and analyze massive datasets in Azure Data Explorer using KQL for logs, telemetry, and time series data. \n \n Execute KQL queries against billions of records with sub-second performance; discover clusters, databases, and table schemas \n Supports five core query patterns: basic retrieval, aggregation analysis, time series analytics, multi-table joins, and schema exploration \n Includes 4 MCP tools (cluster list, database list, query execution, table schema) with Azure CLI fallback for tim