✨ Power BI Modeling MCP Server

The Power BI Modeling MCP Server brings Power BI semantic modeling capabilities to your AI agents through a local MCP server. This allows developers and AI applications to interact with Power BI models in entirely new ways, from using natural language to execute modeling changes to autonomous AI agentic development workflows.

powerbi-modeling-mcp-diagram

💡 What can you do?

🔄 Build and Modify Semantic Models with Natural Language - Tell your AI assistant what you need, and it uses this MCP server to create, update, and manage tables, columns, measures, relationships, and more... across Power BI Desktop and Fabric semantic models.
⚡ Bulk Operations at Scale - AI applications can execute batch modeling operations on hundreds of objects simultaneously — bulk renaming, bulk refactoring, model translations, or model security rules - with transaction support and error handling, turning hours of repetitive work into seconds.
✅ Apply modeling best practices - Easily evaluate and implement modeling best practices against your model.
🤖 Agentic Development Workflows - Supports working with TMDL and Power BI Project files, enabling AI agents to autonomously plan, create, and execute complex modeling tasks across your semantic model codebase.
🔍 Query and Validate DAX - AI assistants can execute and validate DAX queries against your model, helping you test measures, troubleshoot calculations, and explore your data

📹 Watch the video for an end-to-end demo.

Please read before use:

Use caution when connecting an AI Agent to a semantic model. The underlying LLM may produce unexpected or inaccurate results, which could lead to unintended changes. Always create a backup of your model before performing any operations.
LLMs might unintentionally expose sensitive information from the semantic model, including data or metadata, in logs or responses. Exercise caution when sharing chat sessions.
The Power BI Modeling MCP server is in Public Preview and tools may significantly change prior to our General Availability.

See the repository for additional details: https://github.com/microsoft/powerbi-modeling-mcp

🚀 Get started

First, you must connect to a Power BI semantic model, which can reside in Power BI Desktop, Fabric workspace or in Power BI Project (PBIP) files.

For Power BI Desktop:

  Connect to '[File Name]' in Power BI Desktop

For Semantic Model in Fabric Workspace:

  Connect to semantic model '[Semantic Model Name]' in Fabric Workspace '[Workspace Name]'

For Power BI Project files:

  Open semantic model from PBIP folder '[Path to the definition/ TMDL folder in the PBIP]'

Once the connection is established, you can use natural language to ask the AI agent to make any modeling changes. To get started, try one of the following scenarios.

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.`

Note: The scenarios above are just examples. This MCP server equips your agents with modeling tools for any type of model change, and with the right prompt and context, you can automate virtually any modeling task.

💬 Feedback and Support

Check the Troubleshooting guide to diagnose and resolve common issues.
We're building this in the open. Your feedback is much appreciated, and will help us shape the future of the Power BI Modeling MCP server.
- 👉 Open an issue in the public GitHub repository - we’d love to hear from you!

Security

Your credentials are always handled securely through the official Azure Identity SDK - we never store or manage tokens directly.

MCP as a phenomenon is very novel and cutting-edge. As with all new technology standards, consider doing a security review to ensure any systems that integrate with MCP servers follow all regulations and standards your system is expected to adhere to. This includes not only the Power BI Modeling MCP Server, but any MCP client/agent that you choose to implement down to the model provider.

You should follow Microsoft security guidance for MCP servers, including enabling Entra ID authentication, secure token management, and network isolation. Refer to Microsoft Security Documentation for details.

Permissions and Risk

MCP clients can invoke operations based on the user’s Fabric RBAC permissions. Autonomous or misconfigured clients may perform destructive actions. You should review and apply least-privilege RBAC roles and implement safeguards before deployment. Certain safeguards, such as flags to prevent destructive operations, are not standardized in the MCP specification and may not be supported by all clients.

Data Collection

The software may collect information about you and your use of the software and send it to Microsoft. Microsoft may use this information to provide services and improve our products and services. There are also some features in the software that may enable you and Microsoft to collect data from users of your applications. If you use these features, you must comply with applicable law, including providing appropriate notices to users of your applications together with a copy of Microsoft's privacy statement. You can learn more about data collection and use in the help documentation and our privacy statement. Your use of the software operates as your consent to these practices.

Compliance Responsibility

This MCP server may be installed, used and share data with third party clients and services, such as third party LLMs that operate outside Microsoft compliance boundaries. You are responsible for ensuring that any integration complies with applicable organizational, regulatory, and contractual requirements.

Third Party Components

This MCP server may use or depend on third party components. You are responsible for reviewing and complying with the licenses and security posture of any third-party components.

Export Control

Use of this software must comply with all applicable export laws and regulations, including U.S. Export Administration Regulations and local jurisdiction requirements.

No Warranty / Limitation of Liability

This software is provided “as is” without warranties or conditions of any kind, either express or implied. Microsoft shall not be liable for any damages arising from use, misuse, or misconfiguration of this software.

Code of Conduct

This project has adopted the Microsoft Open Source Code of Conduct. For more information, see the Code of Conduct FAQ or contact open@microsoft.com with any additional questions or comments.