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ms-fabric-mcp-server

by bablulawrence
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ms-fabric-mcp-server

PyPI version Python License: MIT Tests

A Model Context Protocol (MCP) server for Microsoft Fabric. Exposes Fabric operations (workspaces, notebooks, SQL, Livy, pipelines, jobs) as MCP tools that AI agents can invoke.

⚠️ Warning: This package is intended for development environments only and should not be used in production. It includes tools that can perform destructive operations (e.g., delete_notebook, delete_item) and execute arbitrary code via Livy Spark sessions. Always review AI-generated tool calls before execution.

Quick Start

The fastest way to use this MCP server is with uvx:

uvx ms-fabric-mcp-server

Installation

# Using uv (recommended) uv pip install ms-fabric-mcp-server # Using pip pip install ms-fabric-mcp-server # With SQL support (requires pyodbc) pip install ms-fabric-mcp-server[sql] # With OpenTelemetry tracing pip install ms-fabric-mcp-server[sql,telemetry]

Authentication

Uses DefaultAzureCredential from azure-identity - no explicit credential configuration needed. This automatically tries multiple authentication methods:

  1. Environment credentials (AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET)

  2. Managed Identity (when running on Azure)

  3. Azure CLI credentials (az login)

  4. VS Code credentials

  5. Azure PowerShell credentials

No Fabric-specific auth environment variables are needed - it just works if you're authenticated via any of the above methods.

Usage

VS Code Integration

Add to your VS Code MCP settings (.vscode/mcp.json or User settings):

{ "servers": { "MS Fabric MCP Server": { "type": "stdio", "command": "uvx", "args": ["ms-fabric-mcp-server"] } } }

Claude Desktop Integration

Add to your claude_desktop_config.json:

{ "mcpServers": { "fabric": { "command": "uvx", "args": ["ms-fabric-mcp-server"] } } }

Codex Integration

Add to your Codex config.toml:

[mcp_servers.ms_fabric_mcp] command = "uvx" args = ["ms-fabric-mcp-server"]

Running Standalone

# Using uvx (no installation needed) uvx ms-fabric-mcp-server # Direct execution (if installed) ms-fabric-mcp-server # Via Python module python -m ms_fabric_mcp_server # With MCP Inspector (development) npx @modelcontextprotocol/inspector uvx ms-fabric-mcp-server

Logging & Debugging (optional)

MCP stdio servers must keep protocol traffic on stdout, so redirect stderr to capture logs. Giving the agent read access to the log file is a powerful way to debug failures. You can also set AZURE_LOG_LEVEL (Azure SDK) and MCP_LOG_LEVEL (server) to control verbosity.

VS Code (Bash):

{ "servers": { "MS Fabric MCP Server": { "type": "stdio", "command": "bash", "args": [ "-lc", "LOG_DIR=\"$HOME/mcp_logs\"; LOG_FILE=\"$LOG_DIR/ms-fabric-mcp-$(date +%Y%m%d_%H%M%S).log\"; uvx ms-fabric-mcp-server 2> \"$LOG_FILE\"" ], "env": { "AZURE_LOG_LEVEL": "info", "MCP_LOG_LEVEL": "INFO" } } } }

VS Code (PowerShell):

{ "servers": { "MS Fabric MCP Server": { "type": "stdio", "command": "powershell", "args": [ "-NoProfile", "-Command", "$logDir=\"$env:USERPROFILE\\mcp_logs\"; New-Item -ItemType Directory -Force -Path $logDir | Out-Null; $ts=Get-Date -Format yyyyMMdd_HHmmss; $logFile=\"$logDir\\ms-fabric-mcp-$ts.log\"; uvx ms-fabric-mcp-server 2> $logFile" ], "env": { "AZURE_LOG_LEVEL": "info", "MCP_LOG_LEVEL": "INFO" } } } }

Programmatic Usage (Library Mode)

from fastmcp import FastMCP from ms_fabric_mcp_server import register_fabric_tools # Create your own server mcp = FastMCP("my-custom-server") # Register all Fabric tools register_fabric_tools(mcp) # Add your own customizations... mcp.run()

Configuration

Environment variables (all optional with sensible defaults):

Variable

Default

Description

FABRIC_BASE_URL

https://api.fabric.microsoft.com/v1

Fabric API base URL

FABRIC_SCOPES

https://api.fabric.microsoft.com/.default

OAuth scopes

FABRIC_API_CALL_TIMEOUT

30

API timeout (seconds)

FABRIC_MAX_RETRIES

3

Max retry attempts

FABRIC_RETRY_BACKOFF

2.0

Backoff factor

LIVY_API_CALL_TIMEOUT

120

Livy timeout (seconds)

LIVY_POLL_INTERVAL

2.0

Livy polling interval

LIVY_STATEMENT_WAIT_TIMEOUT

10

Livy statement wait timeout

LIVY_SESSION_WAIT_TIMEOUT

240

Livy session wait timeout

MCP_SERVER_NAME

ms-fabric-mcp-server

Server name for MCP

MCP_LOG_LEVEL

INFO

Logging level

AZURE_LOG_LEVEL

info

Azure SDK logging level

Copy .env.example to .env and customize as needed.

Available Tools

The server provides 37 core tools, with 3 additional SQL tools when installed with [sql] extras (40 total).

Tool Group

Count

Tools

Workspace

1

list_workspaces

Item

2

list_items, delete_item

Notebook

6

import_notebook_to_fabric, get_notebook_content, attach_lakehouse_to_notebook, get_notebook_execution_details, list_notebook_executions, get_notebook_driver_logs

Job

4

run_on_demand_job, get_job_status, get_job_status_by_url, get_operation_result

Livy

8

livy_create_session, livy_list_sessions, livy_get_session_status, livy_close_session, livy_run_statement, livy_get_statement_status, livy_cancel_statement, livy_get_session_log

Pipeline

7

create_blank_pipeline, add_copy_activity_to_pipeline, add_notebook_activity_to_pipeline, add_dataflow_activity_to_pipeline, add_activity_to_pipeline, delete_activity_from_pipeline, remove_activity_dependency

Semantic Model

7

create_semantic_model, add_table_to_semantic_model, add_relationship_to_semantic_model, get_semantic_model_details, get_semantic_model_definition, add_measures_to_semantic_model, delete_measures_from_semantic_model

Power BI

2

refresh_semantic_model, execute_dax_query

SQL (optional)

3

get_sql_endpoint, execute_sql_query, execute_sql_statement

SQL Tools (Optional)

SQL tools require pyodbc and the Microsoft ODBC Driver for SQL Server:

# Install with SQL support pip install ms-fabric-mcp-server[sql] # On Ubuntu/Debian, install the ODBC driver first: curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add - curl https://packages.microsoft.com/config/ubuntu/$(lsb_release -rs)/prod.list | sudo tee /etc/apt/sources.list.d/mssql-release.list sudo apt-get update sudo ACCEPT_EULA=Y apt-get install -y msodbcsql18

If pyodbc is not available, the server starts with 37 tools (SQL tools disabled).

Development

# Clone and install with dev dependencies git clone https://github.com/your-org/ms-fabric-mcp-server.git cd ms-fabric-mcp-server pip install -e ".[dev,sql,telemetry]" # Run tests pytest # Run with coverage pytest --cov # Format code black src tests isort src tests # Type checking mypy src

Integration tests

Integration tests run against live Fabric resources and are opt-in.

To get started locally, copy the example env file:

cp .env.integration.example .env.integration

Required environment variables:

  • FABRIC_INTEGRATION_TESTS=1

  • FABRIC_TEST_WORKSPACE_NAME

  • FABRIC_TEST_LAKEHOUSE_NAME

  • FABRIC_TEST_SQL_DATABASE

Optional pipeline copy inputs:

  • FABRIC_TEST_SOURCE_CONNECTION_ID

  • FABRIC_TEST_SOURCE_TYPE

  • FABRIC_TEST_SOURCE_SCHEMA

  • FABRIC_TEST_SOURCE_TABLE

  • FABRIC_TEST_DEST_CONNECTION_ID

  • FABRIC_TEST_DEST_TABLE_NAME (optional override; defaults to source table name)

Run integration tests:

FABRIC_INTEGRATION_TESTS=1 pytest

Notes:

  • SQL tests require pyodbc and a SQL Server ODBC driver.

  • Tests may skip when optional dependencies or environment variables are missing.

  • These tests use live Fabric resources and may incur costs or side effects.

License

MIT

Install Server
A
security – no known vulnerabilities
F
license - not found
A
quality - confirmed to work

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