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azure-functions-deploy — Deploy Azure Functions

Deploy your Azure Functions app to Azure by proxying to the Azure Skills deployment workflow.

This skill is the Azure Functions-facing entry point. It should not run deployment commands directly. It prepares Azure Functions-specific context, then delegates execution to azure-prepare, azure-validate, and azure-deploy from the Azure Skills plugin.

Prerequisites

• Azure CLI installed and logged in (az login)
• Azure Developer CLI installed and logged in (azd auth login) for AZD-based deployments
• An Azure subscription (az account show)
• A Functions project with host.json in the current directory
• Azure Skills plugin installed with azure-prepare, azure-validate, and azure-deploy available

If any prerequisite is missing, suggest running azure-functions-setup first.

Required delegation model

Use Azure Skills as the deployment engine:

1. If .azure/deployment-plan.md is missing, invoke azure-prepare first — unless the shortcut path below applies.
2. If .azure/deployment-plan.md exists but is not Validated, invoke azure-validate before any deployment attempt.
3. If .azure/deployment-plan.md exists and status is Validated, invoke azure-deploy directly.
4. Never mark the plan as Validated from this skill. Only azure-validate may do that.
5. Do not run azd up, azd deploy, terraform apply, az deployment, or func azure functionapp publish directly from this skill unless Azure Skills cannot handle the scenario and the user explicitly approves a fallback.

Shortcut path for MCP-template projects

When a project was scaffolded from an Azure MCP template (e.g., via azure-functions-create Path A), it already contains complete infrastructure. Detect this by checking all of these conditions:

1. azure.yaml exists in the project root
2. infra/main.bicep or infra/*.tf exists
3. host.json exists
4. azure.yaml contains a metadata.template field (indicates MCP template origin)

If all conditions are met, skip azure-prepare and proceed directly:

1. Generate a lightweight .azure/plan.md noting the pre-existing infrastructure
2. Invoke azure-validate (provisions preview, environment setup)
3. Invoke azure-deploy (runs azd up)

This avoids the redundant 5–10 minute analysis and planning phase for projects that already have production-ready infrastructure.

The normal workflow is:

azure-functions-deploy → azure-prepare → azure-validate → azure-deploy

When a validated plan already exists:

azure-functions-deploy → azure-deploy

Azure Functions context to inject

Before handing off, collect and pass the following Azure Functions-specific guidance to Azure Skills:

• Use Azure Functions deployment best-practices MCP guidance (get_azure_bestpractices with resource: azurefunctions and action: deployment) when available.
• Prefer Flex Consumption / FC1 for new serverless Function Apps.
• Include functionAppConfig with deployment storage for FC1 Function Apps.
• Use Linux for Python Function Apps.
• Configure Function authentication or stronger mechanisms; avoid anonymous access unless intentional.
• Enable Application Insights for monitoring, exception tracking, and dependency monitoring.
• Consider private networking and one Function App per independently scaling workload when appropriate.
• Use Azure Functions endpoint verification after deploy; do not use curl -I for HTTP trigger verification because HEAD can return false negatives.

Azure Skills plugin installation guidance

If Azure Skills is unavailable, stop and ask the user to install it for their host:

Fallback policy

Prefer Azure Skills for all deployment execution. Use a Functions-specific fallback only when:

• Azure Skills cannot be installed or invoked in the current host,
• the user explicitly asks for a quick publish to an existing Function App, or
• a scenario is not covered by Azure Skills and must be handled by Azure Functions Core Tools.

Before using a fallback, explain what will be skipped compared with azure-prepare → azure-validate → azure-deploy, and ask for confirmation.

Post-deploy verification

Let azure-deploy own post-deploy verification. Add Azure Functions-specific reminders when relevant:

• List deployed functions and verify the app is running.
• Test HTTP trigger endpoints with GET, not HEAD.
• Suggest azure-functions-health-status for runtime health, metrics, and Application Insights checks.
• Suggest azure-functions-best-practices for production readiness after successful deployment.

Monitoring azd up progress

The azd up command uses animated progress bars that may not render correctly in agent terminal capture. If the output appears stuck on a spinner (e.g., repeated "Initialize bicep provider"), the deployment is likely still progressing normally in the background.

To monitor deployment progress independently:

# Check resource group creation
az group show --name rg-<env-name> -o json

# Check deployment status
az deployment group list --resource-group rg-<env-name> \
  --query "[].{name:name, state:properties.provisioningState}" -o table

# Check Function App status
az functionapp list --resource-group rg-<env-name> \
  --query "[].{name:name, state:state}" -o table

Do not kill and restart the azd up process based solely on lack of terminal output. Instead, use the commands above to verify whether the deployment is progressing.

After Deployment

✅ Your app is deployed through Azure Skills. Consider running azure-functions-best-practices for a production readiness review.

Next steps

• On missing Azure Skills, suggest azure-functions-setup to install or configure the Azure Skills plugin.
• On missing or unvalidated plan, invoke azure-prepare and azure-validate before azure-deploy.
• On successful deployment, suggest azure-functions-best-practices for production readiness.
• On deployment failure, suggest azure-functions-diagnostics after azure-deploy error recovery is exhausted.