powerbi

This MCP server provides a Model Context Protocol implementation for integrating Power BI with GenAI applications. It exposes an MCP endpoint that can receive, process, and respond to context-driven requests from client applications, enabling Power BI data visualization and analytics workflows to be orchestrated via MCP messages. The server leverages a Python module (powerbi_mcp) to handle authentication, API interactions, and error handling with retry logic. You can instantiate the server in your Python environment and start listening for MCP messages, allowing downstream GenAI tools to query Power BI data, trigger reports, or retrieve visualization-ready results. The tooling focuses on comprehensive Power BI API coverage, multiple authentication methods, rate limiting for enterprise readiness, and robust asynchronous processing.

Prerequisites:

• Python 3.9+ installed on your system
• PIP available in your environment
• Internet access to fetch packages

Install the MCP server package from PyPI:

pip install powerbi-mcp-server

Alternatively, install from source:

git clone https://github.com/LokiMCPUniverse/powerbi-mcp-server.git
cd powerbi-mcp-server
pip install -e .

Optional: set up a virtual environment to isolate dependencies:

python -m venv .venv
source .venv/bin/activate  # on Windows use .venv\Scripts\activate
pip install -e .

Run the server (as described by the module usage):

python -m powerbi_mcp

Configure environment variables (see additional notes) or set a .env file as needed.

Environment and configuration tips:

• The server expects proper Power BI API credentials; set them via environment variables or a .env file (e.g., POWERBI_API_KEY, POWERBI_API_SECRET).
• If your deployment requires strict access control, implement IP whitelisting and enforce rate limiting provided by the server for enterprise-grade reliability.
• For debugging, enable verbose logging in your environment to trace MCP message handling and API interactions.
• Ensure the module name powerbi_mcp corresponds to the installed package; if you customize the module path, update the -m argument accordingly.
• When running in containerized environments, pass environment variables through your container orchestration tool (Docker, Kubernetes) to avoid hardcoding secrets.