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MCP Engine (for Power BI)

Lets AI assistants query and edit Power BI Desktop files locally on Windows and macOS.

Install MCP Engine (for Power BI)

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Overview

MCP Engine (for Power BI) connects AI assistants to Power BI Desktop files and exposes operations for querying models, creating measures, managing relationships, and optimizing performance. It runs locally on Windows or macOS, with data processing performed on the user's machine and no telemetry collected by the server. Use it when an MCP-compatible assistant needs to inspect or modify Power BI Desktop content through natural-language requests.

Use cases

  • Query Power BI models through natural-language requests
  • Create measures in Power BI Desktop files
  • Manage model relationships with an AI assistant
  • Optimize Power BI model performance
  • Modify local Power BI Desktop files programmatically

Key features

  • Queries Power BI models
  • Creates measures
  • Manages relationships
  • Optimizes performance
  • Runs locally on Windows and macOS
  • Collects no server telemetry

Security & health

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Adoption & maintenance

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GitHub stars255Stargazers on the source repository.
Last commit7d agoMost recent push to the default branch.
Directory activity1 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Engine (for Power BI) works

What MCP Engine (for Power BI) does

MCP Engine (for Power BI) gives MCP-compatible AI assistants programmatic access to Power BI Desktop files. It is intended for working with the semantic model behind a Power BI file rather than serving as a standalone visualization client.

The server supports both read and modification workflows. An assistant can query a model, create measures, manage relationships, and perform performance optimization tasks. These operations can be requested through natural language, allowing the assistant to translate a user's request into actions against the local Power BI Desktop environment.

How it works

The MCP server runs on the user's computer and processes data locally. It communicates with an AI assistant through the Model Context Protocol, while the assistant provides the conversational interface for requesting model queries or changes.

MCP Engine (for Power BI) does not report telemetry according to the project README. However, information included in prompts or returned to the assistant may still be sent to the assistant provider, such as Anthropic or OpenAI, according to that provider's handling of interactions. Local execution therefore limits the server's own data collection but does not prevent an external AI provider from receiving conversation data.

Setup and configuration

The project supports Windows 10 and 11 and macOS. Installation instructions, examples, and troubleshooting guidance are provided through the project's getting-started documentation and FAQ. Releases are available from the repository's releases page.

The README identifies Claude Desktop, Claude Code CLI, Visual Studio Code, and other MCP-compatible clients as supported connection points.

Tools and capabilities

MCP Engine (for Power BI) supports the following documented work areas:

  • Querying a Power BI model
  • Creating measures
  • Managing relationships
  • Optimizing performance
  • Reading and modifying Power BI Desktop files

These capabilities make the server suitable for assistants that need to inspect model structure, apply controlled model changes, or help with model maintenance.

Limitations and notes

The server is designed for Power BI Desktop files and requires Windows or macOS.

The project uses a proprietary license. The product was previously referred to as MCP Engine and is presented in the README under the SemanticOps name. It is distributed as a local server, and its operation depends on an MCP-compatible assistant capable of connecting to it.

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Frequently Asked Questions about MCP Engine (for Power BI)

MCP Engine (for Power BI) is an MCP server that connects AI assistants to Power BI Desktop files on Windows and macOS. Its main tools let agents query models, create measures, manage relationships, and optimize performance programmatically through natural-language requests.

Install it by following the project's getting-started guide and downloading a release from the repository. The documented requirements are Windows 10 or 11, or macOS.

Confirm Cursor's current MCP connection method and the project's installation documentation before deploying it.

It can create measures, manage relationships, and modify Power BI Desktop files. It can also query models and perform documented performance-optimization tasks.

The server itself runs locally and collects no telemetry. Data included in an AI interaction may still be sent to the assistant provider, such as Anthropic or OpenAI, depending on that provider's processing of the conversation.

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