The Power BI MCP Server provides a dedicated interface to manage Power BI modeling tasks through the Model Context Protocol. It exposes a set of tools that allow you to query semantic models, perform bulk operations across tables, columns, and measures, and test security policies and RLS rules. The server is designed to work with both local Power BI Desktop workbooks and cloud-hosted datasets, enabling natural language queries, safe refactoring, and auditing capabilities. You can leverage the PBIP-based editing workflow to rename model elements without breaking visual bindings, thanks to the integrated PBIP connector that updates both the model definitions and report-layer references.

Once the server is running, you can access its tools through the MCP protocol endpoints provided by the server. The 34 tools include security features (PII masking, auditing), access policy controls, a TOM-based connectivity layer for editing, and a dedicated PBIP connector for file-based Power BI edits. This setup enables you to perform end-to-end operations—from discovering dependencies and validating rename impact to executing bulk changes and validating visuals—without leaving your AI-assisted workflow. Integrations with Claude (or other assistants) can drive queries, apply transformations, and monitor changes across your Power BI assets in a coordinated session.

Prerequisites:

• Python 3.10 or higher installed on your system
• Git installed
• Basic familiarity with Power BI PBIP workflow (PBIR-Legacy or PBIR-Enhanced structures) if you plan to use PBIP editing features

Installation steps:

1. Clone the MCP server repository: git clone <repository-url> cd <repository-directory>

2. Create and activate a virtual environment (recommended): python -m venv venv

   Windows

   venv\Scripts\activate.bat

   macOS/Linux

   source venv/bin/activate

3. Install dependencies (adjust if your project uses a different setup, e.g., poetry or pip requirements): python -m pip install -r requirements.txt

4. Install optional development tools (if you plan to run tests or linters): python -m pip install pytest # example

5. Prepare environment variables (example placeholders):

   These are examples; replace with your actual configuration

   export MCP_HOST=localhost export MCP_PORT=8000 export MCP_LOG_LEVEL=INFO

6. Run the MCP server: python -m powerbi_mcp_server

7. Verify the server starts and is listening on the configured port. You should see logs indicating MCP protocol endpoints are ready.

Tips and common issues:

• Ensure Python 3.10+ is used; some dependencies may struggle with older runtimes.
• If using PBIP editing, ensure your PBIP files are accessible with proper read/write permissions and that the path discovery logic can locate the .pbip structure.
• When performing bulk renames, rely on the PBIP connector for the report layer to avoid breaking visuals; verify by opening affected Power BI reports after applying changes.
• For security, configure PII masking and audit logging early to capture sensitive operations and maintain compliance.
• If you encounter module import errors, double-check that your virtual environment is activated and dependencies in requirements.txt are installed.
• If you plan to run the server behind a reverse proxy or inside a container, consider setting appropriate environment variables for host, port, and TLS configuration.