setup-foundry

name	setup-foundry
description	Provision a Microsoft Foundry resource, create a project, deploy a model (gpt-4.1-mini), and install the required Python SDK packages. This is a one-time setup skill. Use when the user says: "setup foundry", "provision foundry", "create foundry resource", "setup azure ai foundry", "install foundry sdk"
metadata	{"verb":"setup"}

Microsoft Foundry Setup

Provision a Foundry resource, project, and model deployment via Azure CLI, then install the Python SDK.

Step 1 -- Verify Azure CLI

az version --output table

If the CLI is not installed, tell the user to install it from https://learn.microsoft.com/cli/azure/install-azure-cli and come back.

Step 2 -- Authenticate

az account show --output table

If not logged in, run:

az login

Step 3 -- Choose Configuration

Ask the user for the following values (or offer sensible defaults):

Setting	Default
Resource group name	foundry-agents-rg
Foundry resource name	foundry-<username> (must be globally unique)
Project name	default
Location	eastus
Model to deploy	gpt-4.1-mini
Deployment name	gpt-4.1-mini

Step 4 -- Create the Resource Group

az group create --name <RESOURCE_GROUP> --location <LOCATION>

Step 5 -- Create the Foundry Resource

az cognitiveservices account create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --kind AIServices \
    --sku S0 \
    --location <LOCATION> \
    --allow-project-management \
    --yes

Step 6 -- Set a Custom Subdomain

The custom subdomain is required for SDK access. It must be globally unique.

az cognitiveservices account update \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --custom-domain <FOUNDRY_RESOURCE_NAME>

Step 7 -- Create the Project

az cognitiveservices account project create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --project-name <PROJECT_NAME> \
    --location <LOCATION>

Step 8 -- Deploy a Model

az cognitiveservices account deployment create \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --deployment-name <DEPLOYMENT_NAME> \
    --model-name <MODEL_NAME> \
    --model-version "2025-04-14" \
    --model-format OpenAI \
    --sku-capacity 10 \
    --sku-name GlobalStandard

If the model version or SKU is not available in the selected region, try Standard SKU or a different model version. You can list available models with:

az cognitiveservices account list-models \
    --name <FOUNDRY_RESOURCE_NAME> \
    --resource-group <RESOURCE_GROUP> \
    --output table

Step 9 -- Install the Python SDK

pip install azure-ai-projects --pre openai azure-identity python-dotenv

Step 10 -- Verify the Setup

Build the project endpoint from the resource and project names:

https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>

Run a quick verification:

python3 -c "
import os
from azure.identity import DefaultAzureCredential
from azure.ai.projects import AIProjectClient

endpoint = 'https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>'
client = AIProjectClient(endpoint=endpoint, credential=DefaultAzureCredential())
openai_client = client.get_openai_client()
resp = openai_client.responses.create(
    model='<DEPLOYMENT_NAME>',
    input='Say hello in one sentence.',
)
print('Model response:', resp.output_text)
print('Setup verified successfully.')
"

Replace the placeholders with the actual values chosen earlier.

Step 11 -- Save Environment Variables

Tell the user to note down or export these values for future use:

export FOUNDRY_PROJECT_ENDPOINT="https://<FOUNDRY_RESOURCE_NAME>.services.ai.azure.com/api/projects/<PROJECT_NAME>"
export FOUNDRY_MODEL_DEPLOYMENT_NAME="<DEPLOYMENT_NAME>"

Summary

Tell the user:

• The Foundry resource, project, and model deployment are ready
• The project endpoint URL (they will need it for the other skills)
• The model deployment name
• The Python SDK packages are installed
• They can now use the Foundry Code Interpreter and Foundry Agent Chat skills