List, find, and show Azure resources across subscriptions or resource groups. Handles prompts like \"list the websites in my subscription\", \"list my web apps\", \"show my app services\", \"list virtual machines\", \"list my VMs\", \"show storage accounts\", \"find container apps\", and \"what resources do I have\". USE FOR: list websites, list web apps, list app services, show websites in subscription, resource inventory, find resources by tag, tag analysis, orphaned resource discovery (not for cost analysis), unattached disks, count resources by type, cross-subscription lookup, and Azure Resource Graph queries. DO NOT USE FOR: deploying/changing resources (use azure-deploy), cost optimization (use azure-cost), or non-Azure clouds.
npx skills add https://github.com/microsoft/skills --skill azure-resource-lookup
List, find, and discover Azure resources of any type across subscriptions and resource groups. Use Azure Resource Graph (ARG) for fast, cross-cutting queries when dedicated MCP tools don't cover the resource type.
Use this skill when the user wants to:
> ⚠️ Warning: App Service / Web Apps have no dedicated MCP list command. Prompts like "list websites", "list web apps", or "list app services" must route through this skill to use Azure Resource Graph.
> 💡 Tip: For single-resource-type queries, first check if a dedicated MCP tool can handle it (see routing table below). If none exists, use Azure Resource Graph.
| Property | Value |
|----------|-------|
| Query Language | KQL (Kusto Query Language subset) |
| CLI Command | az graph query -q "<KQL>" -o table |
| Extension | az extension add --name resource-graph |
| MCP Tool | extension_cli_generate with intent for az graph query |
| Best For | Cross-subscription queries, orphaned resources, tag audits |
| Tool | Purpose | When to Use |
|------|---------|-------------|
| extension_cli_generate | Generate az graph query commands | Primary tool — generate ARG queries from user intent |
| mcp_azure_mcp_subscription_list | List available subscriptions | Discover subscription scope before querying |
| mcp_azure_mcp_group_list | List resource groups | Narrow query scope |
For single-resource-type queries, check if a dedicated MCP tool can handle it:
| Resource Type | MCP Tool | Coverage |
|---|---|---|
| Virtual Machines | compute | ✅ Full — list, details, sizes |
| Storage Accounts | storage | ✅ Full — accounts, blobs, tables |
| Cosmos DB | cosmos | ✅ Full — accounts, databases, queries |
| Key Vault | keyvault | ⚠️ Partial — secrets/keys only, no vault listing |
| SQL Databases | sql | ⚠️ Partial — requires resource group name |
| Container Registries | acr | ✅ Full — list registries |
| Kubernetes (AKS) | aks | ✅ Full — clusters, node pools |
| App Service / Web Apps | appservice | ❌ No list command — use ARG |
| Container Apps | — | ❌ No MCP tool — use ARG |
| Event Hubs | eventhubs | ✅ Full — namespaces, hubs |
| Service Bus | servicebus | ✅ Full — queues, topics |
If a dedicated tool is available with full coverage, use it. Otherwise proceed to Step 2.
Use extension_cli_generate to build the az graph query command:
mcp_azure_mcp_extension_cli_generate
intent: "query Azure Resource Graph to <user's request>"
cli-type: "az"
See Azure Resource Graph Query Patterns for common KQL patterns.
Run the generated command. Use --query (JMESPath) to shape output:
az graph query -q "<KQL>" --query "data[].{name:name, type:type, rg:resourceGroup}" -o table
Use --first N to limit results. Use --subscriptions to scope.
| Error | Cause | Fix |
|-------|-------|-----|
| resource-graph extension not found | Extension not installed | az extension add --name resource-graph |
| AuthorizationFailed | No read access to subscription | Check RBAC — need Reader role |
| BadRequest on query | Invalid KQL syntax | Verify table/column names; use =~ for case-insensitive type matching |
| Empty results | No matching resources or wrong scope | Check --subscriptions flag; verify resource type spelling |
=~ for case-insensitive type matching (types are lowercase)--subscriptions or --first for large tenantsThis skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Advanced GitHub Actions workflow automation with AI swarm coordination, intelligent CI/CD pipelines, and comprehensive repository management
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare applications (RETAIN, SafeDrug, Transformer, GNN).
Take microsoft/azure-resource-lookup from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.