Power BI Authoring MCP Server (Local)

✨ 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.

- The AI model you select directly influences the quality and relevance of the responses you receive. For the best results, choose a deep-reasoning model such as GPT-5 or Claude Sonnet 4.5. You can find more details about available models in the GitHub Copilot AI model comparison.

📦 Installation

The easiest way to install this MCP Server is by using the Visual Studio Code extension extension together with GitHub Copilot. However, you can also manually install it in any other MCP client.

Visual Studio Code (Recommended)

1. Install Visual Studio Code.
2. Install the GitHub Copilot Chat extension.
3. Install the Power BI Authoring MCP Visual Studio Code extension.

1. Open GitHub Copilot chat and confirm the powerbi-modeling-mcp is available and selected.

[!NOTE]

If you do not see powerbi-modeling-mcp in the available tool list, verify that the MCP servers in Copilot option is enabled in Copilot settings on GitHub.com. For enterprise accounts, this option is disabled by default and must be enabled by an administrator.

Manual

This MCP Server can also be configured across other IDEs, CLIs, and MCP clients.

Node Package Executor (NPX) (requires [Node.js](https://nodejs.org/en))

Add the JSON configuration to your MCP client. Node will automatically download the MCP server from the @microsoft/powerbi-modeling-mcp npm package.

{

"powerbi-authoring-local": { "type": "stdio", "command": "npx", "args": [ "-y", "@microsoft/powerbi-modeling-mcp@latest", "--start" ] } }

Accept the EULA

Before you can use the npm package, you must review and accept the Power BI Authoring MCP Server EULA. The MCP server blocks all other tool calls until you accept it.

In an interactive session, your agent can call the accept_eula tool on your behalf after you explicitly authorize it. The server saves the acceptance in a local configuration on your machine, so you are not prompted again.

For unattended execution, accept the EULA through either the command-line argument or environment variable:

• Add --accepteula to the server's args array.
• Set PBI_MODELING_MCP_ACCEPT_EULA to true in the server's environment.

Only use these options after you have reviewed and agreed to the EULA.

Manual download

1. Download the VSIX package for the version you want using the URL below:

• Template: https://marketplace.visualstudio.com/_apis/public/gallery/publishers/analysis-services/vsextensions/powerbi-modeling-mcp/[version]/vspackage?targetPlatform=[platform]
• Example (version 0.1.9, platform win32-x64): https://marketplace.visualstudio.com/_apis/public/gallery/publishers/analysis-services/vsextensions/powerbi-modeling-mcp/0.1.9/vspackage?targetPlatform=win32-x64

1. Rename the downloaded .visx file to .zip
2. Unzip the contents to a folder of your choice, for example: C:\MCPServers\PowerBIAuthoringMCP
3. Run \extension\server\powerbi-modeling-mcp.exe
4. Copy the MCP JSON registration from the console and register it in your preferred MCP client tool.

Example of config that should work in most MCP clients:

{

"powerbi-authoring-local": { "type": "stdio", "command": "C:\\MCPServers\\PowerBIAuthoringMCP\\extension\\server\\powerbi-modeling-mcp.exe", "args": [ "--start" ], "env": {} } }

🚀 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

| 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. |

[!TIP]

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.

🛠️ Avai