Power BI MCP Server 🚀

🎥 Live Demo

Transform your Power BI experience - ask questions in natural language and get instant insights from your data.

A Model Context Protocol (MCP) server that enables AI assistants to interact with Power BI datasets through natural language. Query your data, generate DAX, and get insights without leaving your AI assistant.

✨ Features

• 🔗 Direct Power BI Connection - Connect to any Power BI dataset via XMLA endpoints
• 💬 Natural Language Queries - Ask questions in plain English, get DAX queries and results
• 📊 Automatic DAX Generation - AI-powered DAX query generation using GPT-4o-mini
• 🔍 Table Discovery - Automatically explore tables, columns, and measures
• ⚡ Optimized Performance - Async operations and intelligent caching
• 🛡️ Secure Authentication - Service Principal authentication with Azure AD
• 📈 Smart Suggestions - Get relevant question suggestions based on your data

🎥 Demo

Ask questions like "What are total sales by region?" and get instant insights from your Power BI data.

🚀 Quick Start

System Requirements & Platform Compatibility

Platform	Python	.NET Runtime	ADOMD.NET	Status
Windows	3.10+	✅ Built-in	✅ Available	✅ Full Support
Linux	3.10+	✅ Available	⚠️ Docker only	✅ Docker Support
macOS	3.10+	✅ Available	❌ Not available	❌ Not supported

Note: For Linux systems, use Docker to run the server with all dependencies included.

Prerequisites

• Python 3.10 or higher (3.8+ may work but not officially supported)
• Windows with ADOMD.NET or Docker on Linux (container includes the runtime)
• SQL Server Management Studio (SSMS) or ADOMD.NET client libraries (Windows only)
• Power BI Pro/Premium with XMLA endpoint enabled
• Azure AD Service Principal with access to your Power BI dataset
• OpenAI API key (optional for natural language features)

Installation

1. Clone the repository

   git clone https://github.com/yourusername/powerbi-mcp-server.git
   cd powerbi-mcp-server

2. Install dependencies

   pip install -r requirements.txt

3. Configure environment variables

   cp .env.example .env
   # Edit .env with your credentials

4. Test the connection

   python quickstart.py

Configure with Claude Desktop

Add to your Claude Desktop configuration file:

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "powerbi": {
      "command": "python",
      "args": ["C:/path/to/powerbi-mcp-server/src/server.py"],
      "env": {
        "PYTHONPATH": "C:/path/to/powerbi-mcp-server",
        "OPENAI_API_KEY": "your-openai-api-key"
      }
    }
  }
}

Docker

⚠️ Important: Docker containers do NOT use .env files. The .env file is excluded from the Docker build context for security. You must provide environment variables via docker run -e, Docker Compose, or your cloud platform.

Build the container image:

docker build -t powerbi-mcp .

Run the server:

docker run -it --rm -e OPENAI_API_KEY=<key> powerbi-mcp

The container listens on port 8000 by default. Override the host or port using environment variables or command-line arguments:

docker run -it --rm -e OPENAI_API_KEY=<key> -p 7000:7000 powerbi-mcp \
  python src/server.py --host 0.0.0.0 --port 7000

The server exposes a Server-Sent Events endpoint at /sse. Clients should connect to this endpoint and then POST JSON-RPC messages to the path provided in the initial endpoint event (typically /messages/).

The container includes the .NET runtime required by pythonnet and pyadomd. It sets PYTHONNET_RUNTIME=coreclr and DOTNET_ROOT=/usr/share/dotnet so the .NET runtime is detected automatically.

Important: The Docker container does NOT use .env files. Any .env file in your local directory will be excluded from the Docker image via .dockerignore for security reasons. Instead, provide environment variables using:

• docker run -e VARIABLE=value
• Docker Compose environment variables
• Cloud platform environment variable injection

The available environment variables mirror those in .env.example.

📖 Usage

Once configured, you can interact with your Power BI data through Claude:

Connect to Your Dataset

Connect to Power BI dataset at powerbi://api.powerbi.com/v1.0/myorg/YourWorkspace

Explore Your Data

What tables are available?
Show me the structure of the Sales table

Ask Questions

What are the total sales by product category?
Show me the trend of revenue over the last 12 months
Which store has the highest gross margin?

Execute Custom DAX

Execute DAX: EVALUATE SUMMARIZE(Sales, Product[Category], "Total", SUM(Sales[Amount]))

🔧 Configuration

Required Credentials

1. Power BI XMLA Endpoint

   • Format: powerbi://api.powerbi.com/v1.0/myorg/WorkspaceName
   • Enable in Power BI Admin Portal → Workspace Settings

2. Azure AD Service Principal

   • Create in Azure Portal → App Registrations
   • Grant access in Power BI Workspace → Access settings

3. OpenAI API Key (optional)

   • Needed only for natural language features
   • Endpoints that rely on GPT models are hidden if this key is not set
   • Get from OpenAI Platform
   • Model used: gpt-4o-mini (200x cheaper than GPT-4)

Environment Variables

Create a .env file (OpenAI settings are optional):

# OpenAI Configuration (optional)
OPENAI_API_KEY=your_openai_api_key_here
OPENAI_MODEL=gpt-4o-mini  # Defaults to gpt-4o-mini

# Optional: Default Power BI Credentials
# These values are used when the `connect_powerbi` action does not supply
# tenant_id, client_id or client_secret.
DEFAULT_TENANT_ID=your_tenant_id
DEFAULT_CLIENT_ID=your_client_id
DEFAULT_CLIENT_SECRET=your_client_secret

# Logging
LOG_LEVEL=INFO

🏗️ Architecture

powerbi-mcp-server/
├── src/
│   └── server.py          # Main MCP server implementation
├── docs/                  # Documentation
├── examples/              # Example queries and use cases
├── tests/                 # Test suite
├── .env.example          # Environment variables template
├── requirements.txt      # Python dependencies
├── quickstart.py        # Quick test script
└── README.md           # This file

Key Components

1. PowerBIConnector - Handles XMLA connections and DAX execution
2. DataAnalyzer - AI-powered query generation and interpretation
3. PowerBIMCPServer - MCP protocol implementation

🔐 Security Best Practices

• Never commit credentials - Use .env files and keep them in .gitignore
• Use Service Principals - Avoid personal credentials
• Minimal permissions - Grant only necessary access to datasets
• Rotate secrets regularly - Update Service Principal secrets periodically
• Use secure connections - Always use HTTPS/TLS

🧪 Testing

Unit Tests

Run the standard test suite:

python -m pytest tests/

Test specific functionality:

python tests/test_connector.py
python tests/test_server_process.py

Integration Tests

Real integration tests with Power BI datasets are available but disabled by default. These tests connect to actual Power BI services and may consume API quota.

Enable Integration Tests:

1. Configure test environment

   cp .env.example .env
   # Edit .env file and set:
   ENABLE_INTEGRATION_TESTS=true

2. Set test dataset configuration

   # Test Power BI Dataset Configuration
   TEST_XMLA_ENDPOINT=powerbi://api.powerbi.com/v1.0/myorg/YourTestWorkspace
   TEST_TENANT_ID=your_tenant_id
   TEST_CLIENT_ID=your_client_id  
   TEST_CLIENT_SECRET=your_client_secret
   TEST_INITIAL_CATALOG=YourTestDatasetName
   
   # Optional: Expected test data for validation
   TEST_EXPECTED_TABLE=Sales
   TEST_EXPECTED_COLUMN=Amount
   TEST_DAX_QUERY=EVALUATE TOPN(1, Sales)
   TEST_MIN_TABLES_COUNT=1

3. Run integration tests

   # Interactive runner with safety checks
   python run_integration_tests.py
   
   # Or directly with pytest
   python -m pytest tests/test_integration.py -v
   
   # Run with auto-confirmation (CI/CD)
   python run_integration_tests.py --yes

Integration Test Coverage:

• ✅ Power BI dataset connection
• ✅ Table discovery and schema retrieval
• ✅ DAX query execution
• ✅ Sample data retrieval
• ✅ MCP tool interface testing
• ✅ Natural language query generation (with OpenAI)
• ✅ AI-powered suggestions (with OpenAI)

⚠️ Warning: Integration tests connect to real Power BI datasets and may consume:

• XMLA endpoint usage quota
• OpenAI API tokens
• Network bandwidth

Only run integration tests in dedicated test environments.

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for details.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📊 Performance

• Connection time: 2-3 seconds
• Query execution: 1-5 seconds depending on complexity
• Token usage: ~500-2000 tokens per query with GPT-4o-mini
• Cost: ~$0.02-0.06 per day for typical usage

🧪 Testing

Running Tests

# Check environment compatibility
python scripts/check_test_environment.py

# Run unit tests
python -m pytest tests/ -k "not test_integration" -v

# Run integration tests (requires .env configuration)
python -m pytest tests/test_integration.py -v

Test Environment Requirements

• Python 3.10+ (recommended)
• All dependencies from requirements.txt
• For integration tests: valid Power BI connection credentials

Platform-Specific Testing

• Windows: Full test suite supported
• Linux: Unit tests only (use Docker for integration tests)
• macOS: Unit tests only (limited support)

🐛 Troubleshooting

Common Issues

1. ADOMD.NET not found

   • For Windows, install SQL Server Management Studio (SSMS)
   • On Linux, use the provided Docker image which bundles the cross-platform ADOMD.NET runtime

2. Connection fails

   • Verify XMLA endpoint is enabled in Power BI
   • Check Service Principal has workspace access
   • Ensure dataset name matches exactly

3. Timeout errors

   • Increase timeout in Claude Desktop config
   • Check network connectivity to Power BI

See TROUBLESHOOTING.md for detailed solutions.

📝 License

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

🙏 Acknowledgments

• Anthropic for the MCP specification
• Microsoft for Power BI and ADOMD.NET
• OpenAI for GPT models
• The MCP community for inspiration and support

📬 Support

• 📧 Email: [email protected]
• 💬 Issues: GitHub Issues
• 📚 Docs: Full Documentation