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Model Context Protocol (MCP) server for Microsoft Fabric - exposes Fabric operations as MCP tools for AI agents

Project description

ms-fabric-mcp-server

PyPI version Python License: MIT

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"]
    }
  }
}

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

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

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

Available Tools

The server provides 25 core tools, with 3 additional SQL tools when installed with [sql] extras (28 total).

Tool Group Count Tools
Workspace 2 list_workspaces, create_workspace
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 3 create_blank_pipeline, add_copy_activity_to_pipeline, add_activity_to_pipeline
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 25 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

License

MIT

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