Model Context Protocol: Powering the Next Wave of AI in Power BI

Artificial intelligence has long promised to revolutionize Power BI, but tangible results have often lagged behind the buzz. Today, innovations like the model context protocol (MCP) are finally delivering on that potential. 

These tools empower both developers and business users to interact with Power BI and Microsoft Fabric using natural language, opening new doors for productivity and accessibility in analytics.

In a recent article SQLBi lays out how you can use PowerBI or fabric MCP servers to explore data, execute queries and more. Check out the full detailed instructions and article here.

Shifting the Landscape of AI in Power BI

Historically, AI features in Power BI served two primary groups: creators (developers) and consumers (business users). For creators, tools like Copilot and advanced language models (think ChatGPT, Claude, and Gemini) have made it easier to generate DAX/M code, troubleshoot, and document solutions. 

For consumers, AI has enabled Conversational BI, letting users query data and get answers as text, tables, or visuals. Yet, these tools often required heavy model preparation, limiting their overall effectiveness and reach.

MCP Servers: The Breakthrough

The introduction of MCP servers is changing how we interact with our data. Acting as intermediaries between language models and Power BI or Fabric, MCP servers let users perform complex actions through natural language, whether from desktop apps like Claude Desktop or development environments like VS Code. 

What sets MCP servers apart?

• Support for full CRUD operations (create, read, update, and delete) across Power BI workspaces and items
  
  
• Natural language control that doesn't require special Fabric capacity and runs on local or remote machines
  
  
• Simultaneous integration with various tools and data sources
  
  
• Automation of repetitive or intricate tasks, freeing users to focus on analysis

MCP servers work by exposing resources (such as data schemas), tools (like query execution), and prompts (standardized instructions) to language models. This enables agents to reason, take multi-step actions, and merge information from diverse sources all via a user-friendly chat interface.

Beyond Traditional Conversational BI

Traditional conversational BI in Power BI is often constrained to a single-question-single-query approach, limited customization, and basic visuals which all requiring significant model prep. MCP server-powered language models offer a more dynamic reasoning flow:

• Run multiple queries and refine answers based on intermediate results
  
  
• Draw on external context and combine information from multiple platforms
  
  
• Customize visuals and iterate outputs on the fly
  
  
• Reduce up-front model prep, using persistent memory to build context over time

This flexibility enables business professionals and analysts to uncover insights, solve problems, and automate tasks with far less technical friction. Organizations can also tailor their AI experiences by developing custom MCP servers that leverage Power BI and Fabric APIs.

What This Means for Analytics and Reporting

As conversational BI evolves, classic dashboards and reports remain essential for rapid, high-level business overviews. The future is likely a hybrid: dashboards deliver fast access to key metrics, while conversational BI, supercharged by MCP servers, offers deeper exploration and refined interactions. 

Still, there are key challenges to keep in mind:

• Language model responses can vary, even with identical prompts
• AI tools may hallucinate or return incorrect answers
• This technology is emerging, and best practices are still developing

Designing and implementing MCP servers thoughtfully can help address these concerns, but ongoing validation and user education remain crucial.

A New Era for Power BI

The rise of MCP servers signals a major leap forward for AI-driven analytics in Power BI. By making natural language control over data more seamless and powerful, MCP servers are poised to make business intelligence not just smarter, but also more accessible and adaptable for everyone. Watch for conversational BI to become even more sophisticated and integrated into daily workflows while still relying on traditional dashboards as a vital complement.