[! 중요] Fabric 작업 공간에서 의미 모델을 작성할 때는 원격(호스팅된) Power BI 저작 MCP 서버를 사용하세요. 로컬 설치가 필요 없으며, Microsoft가 업데이트를 관리합니다. 원격 및 로컬 옵션을 비교하려면 Power BI 저작 MCP 서버를 참조하세요.

Power BI 저작 MCP 서버는 MCP 명세를 구현하여 AI 에이전트와 Power BI 의미 모델 간의 원활한 연결을 만듭니다.

Power BI 저작 MCP 서버는 로컬 MCP 서버를 통해 AI 에이전트에게 Power BI 의미 모델링 기능을 제공합니다. 이를 통해 개발자와 AI 애플리케이션은 자연어를 사용해 모델링 변경을 실행하거나 자율적인 AI 에이전트 개발 워크플로우를 실행하는 등 완전히 새로운 방식으로 Power BI 모델과 상호작용할 수 있습니다.

💡 어쩔 수 없지?

• ** 🔄 자연어로 의미 모델을 구축하고 수정하세요** - AI 어시스턴트에게 필요한 것을 알려주면, 이 MCP 서버를 사용해 테이블, 열, 측정값, 관계 등 다양한 항목을 생성, 업데이트, 관리합니다… Power BI 데스크톱과 Fabric 시맨틱 모델 전반에 걸쳐 적용됩니다.

✨ Power BI Authoring MCP Server

[!IMPORTANT] When authoring semantic models in a Fabric workspace, use the remote (hosted) Power BI Authoring MCP server. It requires no local installation, and Microsoft manages updates. See Power BI Authoring MCP server to compare the remote and local options.

The Power BI Authoring MCP Server implements the MCP specification to create a seamless connection between AI agents and Power BI semantic models.

The Power BI Authoring 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.

💡 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.

[!WARNING]

• 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. See Data Privacy and LLM Providers.
• The Power BI Authoring MCP server can only execute modeling operations. It cannot modify other types of Power BI metadata, such as report pages or semantic model elements like diagram layouts.