What if your AI agents could do more than just analyze data in Power BI? With the Power BI Modeling MCP Server extension AI agents can now actively build, modify, and manage semantic models using simple natural language, eliminating tedious manual processes and unlocking automation at scale. This breakthrough tool is transforming how developers and organizations approach data modeling in Power BI.
Powerful Features That Set MCP Server Apart
- Conversational Semantic Model Management: AI agents interact directly with Power BI models, making changes to tables, columns, relationships, and measures through conversational prompts with no manual clicks needed.
- Batch Model Operations: Execute bulk tasks such as renaming, refactoring, or enforcing security rules across hundreds of objects in one go. This speeds up repetitive workflows and ensures consistency.
- Automated Best Practices: The extension assesses and helps implement modeling best practices, so your Power BI models stay robust and optimized.
- Agentic Development Workflows: With support for TMDL and Power BI Project files, AI agents can autonomously plan, create, and execute sophisticated modeling changes across entire codebases.
- DAX Query Validation: AI assistants can validate DAX queries, troubleshoot issues, and help users explore data more effectively within Power BI.
Getting Started with MCP Server
To begin, connect the MCP server to your Power BI semantic model, whether it’s in Power BI Desktop, a Fabric workspace, or within Power BI Project files. Once connected, simply instruct your AI agent using natural language to make the changes you need with no deep coding knowledge required.
Real-World Application Scenarios
- Enforce and standardize naming conventions across your models
- Automatically generate comprehensive documentation, including descriptions and relationship diagrams
- Translate entire models (tables, columns, measures) into multiple languages for global collaboration
- Refactor measures and queries, optimize calculation groups, or parameterize data sources with minimal effort
- Benchmark DAX queries across different versions to validate performance and accuracy
These use cases demonstrate the MCP server’s versatility. With the right prompts and configuration, nearly any modeling task can be automated and streamlined.
Example scenarios
| Scenario | Prompt examples |
|---|---|
| Analyze naming convention and bulk rename. | Analyze my model’s naming conventions and suggest renames to ensure consistency. Analyze the naming convention of the ‘Sales’ table and apply the same pattern across the entire model. |
| Set descriptions across your model for documentation. | Add descriptions to all measures, columns, and tables to clearly explain their purpose and explain the logic behind the DAX code in simple, understandable terms. |
| Translate your semantic model. | Generate a French translation for my model including tables, columns and measures. |
| Refactor measures into Calculation Groups or UDF. | Refactor measures 'Sales Amount 12M Avg' and 'Sales Amount 6M Avg' into a calculation group and include new variants: 24M and 3M. |
| Refactor your queries to use semantic model parameters. | Analyze the Power Query code for all tables, identify the data source configuration, and create semantic model parameters to enable easy switching of the data source location. |
| Benchmark DAX queries against multiple models. | Connect to semantic model 'V1' and 'V2. And benchmark the following DAX query against both models. [DAX Query] |
| Document your semantic model | Generate a Markdown document (.md) that provides complete, professional documentation for a Power BI Semantic Model. Use a simple mermaid diagram to ilustrate the table relationships; Document each measure including the DAX code and a description of the business logic using business friendly names; Document row level filters; Document the data sources by analyzing the Power Query code. |
Security and Compliance Take Center Stage
Given the extension’s powerful capabilities, security is a top priority. The MCP server uses the Azure Identity SDK for credential management, and users should always back up models before making changes. Connecting AI agents means LLMs could introduce unexpected changes or expose sensitive data, so apply caution and best practices.
Implement Entra ID authentication, use secure token management, and isolate networks to minimize risks. RBAC permissions are enforced, making it essential to follow least-privilege principles and implement safeguards to prevent unwanted or destructive actions.
Additionally, the extension may collect usage data to help Microsoft improve its offerings. For those integrating third-party LLMs or components, ensure compliance with company policies, regulatory guidelines, export laws, and third-party licenses.
Community-Driven Development and Feedback
The Power BI Modeling MCP Server is currently in public preview, and Microsoft welcomes user feedback via GitHub. Comprehensive documentation, troubleshooting support, and a strong adherence to the Microsoft Open Source Code of Conduct foster a collaborative and supportive user community.
Takeaway: AI-Powered Modeling for the Future
The Power BI Modeling MCP Server extension brings unprecedented automation, flexibility, and intelligence to data modeling in Power BI. By integrating AI agents into the workflow, developers can scale complex modeling tasks, enforce best practices, and drive data innovation—while maintaining robust security and compliance. Whether refining a single model or orchestrating changes across an enterprise, this extension is set to redefine what’s possible with Power BI semantic modeling.
Source: Power BI Modeling MCP Server - Visual Studio Marketplace
Revolutionizing Power BI Semantic Modeling with AI Agents and the MCP Server Extension