Microsoft Data Factory MCP Server

A Model Context Protocol (MCP) server for Microsoft Fabric resource discovery and information retrieval. This server provides tools for authentication and accessing Microsoft Fabric resources through a standardized MCP interface.

Features

• Azure AD Authentication: Interactive and service principal authentication
• Gateway Management: List, retrieve, and create Microsoft Fabric gateways (including VNet gateways)
• Connection Management: List, retrieve, and create Microsoft Fabric connections (cloud, on-premises, and VNet)
• Workspace Management: List and retrieve Microsoft Fabric workspaces
• Dataflow Management: List, create, and retrieve Microsoft Fabric dataflows
• Pipeline Management: List, create, update, run, monitor, and schedule Microsoft Fabric pipelines
• Copy Job Management: List, create, update, run, monitor, and schedule Microsoft Fabric copy jobs
• Capacity Management: List and retrieve Microsoft Fabric capacities
• Microsoft Fabric Integration: Support for on-premises, personal, and virtual network gateways
• 📦 NuGet Distribution: Available as a NuGet package for easy integration
• 🔧 MCP Protocol: Built using the official MCP C# SDK

Available Tools

• Authentication: authenticate_interactive, authenticate_service_principal, get_authentication_status, get_access_token, sign_out
• Gateway Management: list_gateways, get_gateway, create_virtualnetwork_gateway
• Connection Management: list_supported_connection_types, list_connections, get_connection, create_connection
• Workspace Management: list_workspaces
• Dataflow Management: list_dataflows, create_dataflow, get_dataflow_definition, add_connection_to_dataflow, add_or_update_query_in_dataflow, save_dataflow_definition
• Dataflow Refresh: refresh_dataflow_background, refresh_dataflow_status
• Dataflow Query Execution: execute_query (Preview)
• Capacity Management: list_capacities
• Pipeline Management: list_pipelines, create_pipeline, get_pipeline, update_pipeline, get_pipeline_definition, update_pipeline_definition, run_pipeline, get_pipeline_run_status, create_pipeline_schedule, list_pipeline_schedules (Preview)
• Copy Job Management: list_copy_jobs, create_copy_job, get_copy_job, update_copy_job, get_copy_job_definition, update_copy_job_definition, run_copy_job, get_copy_job_run_status, create_copy_job_schedule, list_copy_job_schedules (Preview)

Available Resources

The server exposes interactive UI forms as MCP App resources, rendered directly inside VS Code chat:

Resource URI	Description
ui://datafactory/create-connection	Guided wizard for creating a new data source connection (details)

Quick Start

Using from NuGet (Recommended)

1. Configure your IDE: Create an MCP configuration file in your workspace:

   VS Code: Create .vscode/mcp.json Visual Studio: Create .mcp.json in solution directory

   {
     "servers": {
       "DataFactory.MCP": {
         "type": "stdio",
         "command": "dnx",
         "args": [
           "Microsoft.DataFactory.MCP",
           "--version",
           "#{VERSION}#",
           "--yes"
         ]
       }
     }
   }

2. Start using: The server will be automatically downloaded and available in your IDE's MCP-enabled chat interface.

Development Setup

To run the server locally during development:

{
  "servers": {
    "DataFactory.MCP": {
      "type": "stdio",
      "command": "dotnet",
      "args": [
        "run",
        "--project",
        "path/to/DataFactory.MCP"
      ]
    }
  }
}

Configuration

Prerequisites

• .NET 10.0 or later
• Azure AD tenant and application registration with appropriate permissions
• Environment variables for authentication (see Authentication Guide for setup details)

Usage Examples

See the detailed guides for comprehensive usage instructions:

• Authentication: See Authentication Guide
• Gateway Management: See Gateway Management Guide
• Connection Management: See Connection Management Guide
• Workspace Management: See Workspace Management Guide
• Dataflow Management: See Dataflow Management Guide
• Capacity Management: See Capacity Management Guide
• Pipeline Management: See Pipeline Management Guide
• Copy Job Management: See Copy Job Management Guide

Development

Building the Project

# Restore dependencies
dotnet restore

# Build the project
dotnet build

# Create NuGet package
dotnet pack -c Release

Testing Locally

1. Configure your IDE with the development configuration shown above
2. Run the project: dotnet run
3. Test the tools through your MCP-enabled chat interface

Claude Skills (Optional)

Enhance your Claude experience with pre-built Data Factory skills that provide operational tips and best practices.

Installation

Upload the skill files from the claude-skills/ folder to your Claude Project:

1. Go to your Claude Project settings
2. Add these files to Project Knowledge:
   • datafactory-SKILL.md - Index file (always loaded)
   • datafactory-core.md - M basics, MCP tools overview
   • datafactory-performance.md - Query optimization, timeouts, chunking
   • datafactory-destinations.md - Output configuration, programmatic setup
   • datafactory-advanced.md - Fast Copy, Action.Sequence, Modern Evaluator

What's Covered

Skill	Topics
Core	M (Power Query) fundamentals, Dataflow Gen2 overview, MCP tool reference, connection management
Performance	Query timeouts, chunking strategies, filter optimization, connector selection
Destinations	Lakehouse architecture, schema settings, programmatic destination configuration
Advanced	Action.Sequence for writes, Fast Copy, Modern Evaluator

Usage

Once installed, Claude will automatically reference these skills based on your questions:

• "My query is timing out" → loads performance tips
• "How do I set the output destination programmatically?" → loads destination guide
• "What's Fast Copy?" → loads advanced features

ChatGPT Skills (Optional)

Create a Custom GPT or use ChatGPT Projects with pre-built Data Factory knowledge.

Installation

1. Go to ChatGPT → Explore GPTs → Create
2. Copy instructions from chatgpt-skills/gpt-instructions.md
3. Upload knowledge files from chatgpt-skills/:
   • knowledge-core.md - M basics, Dataflow Gen2 overview
   • knowledge-performance.md - Query optimization, timeouts
   • knowledge-destinations.md - Output configuration
   • knowledge-advanced.md - Fast Copy, Action.Sequence

See chatgpt-skills/README.md for detailed setup options.

Documentation

For complete documentation, see our Documentation Index.

Quick Links

• Authentication Guide - Complete authentication setup and usage
• Gateway Management Guide - Gateway operations and examples
• Connection Management Guide - Connection operations and examples
• Workspace Management Guide - Workspace operations and examples
• Dataflow Management Guide - Dataflow operations and examples
• Capacity Management Guide - Capacity operations and examples
• Pipeline Management Guide - Pipeline operations and examples
• Copy Job Management Guide - Copy job operations and examples
• Architecture Guide - Technical architecture and design details

Contributing

We welcome contributions! To get started:

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Please follow standard .NET coding conventions and ensure all tests pass before submitting.

Extension Points

The server is designed for extensibility. For detailed information on extending functionality, see the Extension Points section in our architecture documentation, which covers:

• Adding New Tools: Create custom MCP tools for additional operations
• Adding New Services: Implement new services following our patterns
• Service Registration: Proper dependency injection setup

This modular architecture makes it easy to add support for additional Azure services or custom business logic.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For issues and questions:

• Create an issue in this repository
• Review the MCP documentation