ms-fabric

Microsoft Fabric Management

Fabric has no dedicated CLI. Use the Fabric REST API via az rest or Python SDKs. The user authenticates via az login.

Current context

• Azure account: !az account show --query "{name:name, id:id}" -o tsv 2>/dev/null || echo "not logged in — run: az login"

Safety rules & circuit breaker

• ALWAYS confirm before deleting workspaces, items, or triggering deployments
• Flag production workspaces — if a workspace name contains "prod", "prd", or "production", warn explicitly
• Show workspace name and item ID before any modification

For bulk or iterative operations (refreshing multiple models, deploying across workspaces, deleting items), apply self-regulation from shared safety patterns:

• Track a mental risk score starting at 0%
• Each operation on a production workspace: +15% risk
• Each failed refresh or deployment: +15% risk
• Operations outside the originally-specified workspace: +20% risk
• Risk > 20%: Stop and confirm with the user
• Hard cap: Never execute more than 50 API calls in a modification loop without a user checkpoint

REST API via az rest

The Fabric REST API base URL is https://api.fabric.microsoft.com/v1.

Workspaces

# List all workspaces
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces"

# Get a specific workspace
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>"

# List items in a workspace
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items"

# Filter items by type (Lakehouse, Warehouse, SemanticModel, Notebook, DataPipeline, etc.)
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items?type=Lakehouse"

Lakehouses

# List lakehouses in a workspace
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/lakehouses"

# List tables in a lakehouse
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/lakehouses/<lakehouse-id>/tables"

# Load a file into a table
az rest --method POST --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/lakehouses/<lakehouse-id>/tables/<table-name>/load" --body '{"relativePath": "Files/data.csv", "pathType": "File", "mode": "Overwrite"}'

Semantic models

# List semantic models
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/semanticModels"

# Trigger refresh
az rest --method POST --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/semanticModels/<model-id>/refresh"

# Get refresh history
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/semanticModels/<model-id>/refreshes"

Pipelines (Data Pipelines)

# List pipelines
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items?type=DataPipeline"

# Run a pipeline (via item job)
az rest --method POST --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items/<pipeline-id>/jobs/instances?jobType=Pipeline"

# Get job instances
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items/<item-id>/jobs/instances"

Notebooks

# List notebooks
az rest --method GET --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items?type=Notebook"

# Get notebook definition
az rest --method POST --url "https://api.fabric.microsoft.com/v1/workspaces/<workspace-id>/items/<notebook-id>/getDefinition"

For a full API reference, see api-reference.md.

Python SDK: microsoft-fabric-api

pip install microsoft-fabric-api

from microsoft.fabric import FabricClient
from azure.identity import DefaultAzureCredential

client = FabricClient(credential=DefaultAzureCredential())

# List workspaces
workspaces = client.workspaces.list()
for ws in workspaces:
    print(f"{ws.display_name} ({ws.id})")

# List items in a workspace
items = client.items.list(workspace_id="<workspace-id>")
for item in items:
    print(f"{item.type}: {item.display_name}")

Python SDK: semantic-link (sempy)

For querying semantic models and running DAX. Best used in Fabric notebooks or local environments with az login.

pip install semantic-link semantic-link-labs

import sempy.fabric as fabric

# List datasets in a workspace
datasets = fabric.list_datasets(workspace="<workspace-name>")

# Read a table from a semantic model
df = fabric.read_table(
    dataset="<dataset-name>",
    table="<table-name>",
    workspace="<workspace-name>"
)

# Run a DAX query
result = fabric.evaluate_dax(
    dataset="<dataset-name>",
    dax_string='EVALUATE SUMMARIZECOLUMNS(\'Date\'[Year], "Total Sales", [Total Sales])',
    workspace="<workspace-name>"
)

CI/CD with fabric-cicd

pip install fabric-cicd

The fabric-cicd tool is officially supported by Microsoft for deploying Fabric items across workspaces (dev -> test -> prod).

from fabric_cicd import FabricWorkspace, publish_all_items

# Connect to source workspace
ws = FabricWorkspace(
    workspace_id="<workspace-id>",
    repository_directory="<local-path>",
    item_type_in_scope=["Notebook", "DataPipeline", "Lakehouse"]
)

# Deploy items
publish_all_items(workspace=ws)

Helper scripts

For reusable Python helpers, see scripts/fabric_helpers.py.

Optional: Fabric MCP Server

For richer integration via GraphQL:

• Requires: GraphQL API enabled in Fabric workspace, Service Principal auth
• Repository: microsoft/fabric-samples (MCP server)
• Setup: Enable GraphQL introspection (requires Workspace Admin), configure Service Principal in .env
• See: https://blog.fabric.microsoft.com/en-us/blog/connecting-ai-agents-to-microsoft-fabric-with-graphql-and-the-model-context-protocol-mcp