[!重要] Fabricワークスペースでセマンティックモデルを作成する際は、リモート(ホストされている)Power BI Authoring MCPサーバーを使用します。ローカルインストールは不要で、Microsoftがアップデートを管理します。リモートとローカルのオプションを比較するにはPower BI Authoring MCPサーバーを参照してください。

Power BI Authoring MCP Serverは、MCP仕様を実装し、AIエージェントとPower BIセマンティックモデル間のシームレスな接続を実現します。

Power BI Authoring MCP Serverは、ローカルMCPサーバーを通じて、Power BIの意味モデリング機能をAIエージェントに提供します。これにより、開発者やAIアプリケーションは、自然言語を使ったモデリング変更の実行から自律的なAIエージェント開発ワークフローまで、まったく新しい方法でPower BIモデルとやり取りできるようになります。

💡 どうすればいい?

• ** 🔄 自然言語で意味モデルを作成・修正** - 必要なものをAIアシスタントに伝えると、このMCPサーバーを使ってテーブル、列、測定、関係などを作成、更新、管理します…Power BI DesktopおよびFabricのセマンティックモデル全体で。

✨ 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.