If learning KQL has kept you from diving into real-time analytics, the MCP Server for Real-Time Intelligence in Microsoft Fabric is here to help. This new tool translates plain English into KQL queries, giving you instant access to insights without having to learn the language first.
What Is the MCP Server?
The MCP Server acts as a natural language interface for Microsoft Fabric’s Real-Time Intelligence capabilities.
It translates plain English questions into KQL queries
Executes those queries against your Eventhouse
Returns real-time answers no KQL skills required
Step-by-Step: How to Set Up the MCP Server
You’ll find the official MCP Server GitHub page in the Fabric Real-Time Intelligence documentation.
Requirements:
- Visual Studio Code
- GitHub Copilot Chat extension
- PowerShell module to run Python packages without virtual environments
Installation Steps:
- Open an empty folder in VS Code
- Run the command:
Ctrl + Shift + P → MCP: Add Server - Choose Pip Package
- Install the package
microsoft-fabric-rti-mcp - Set the server ID (e.g.,
fabric-rti-mcp) - Choose local or global install
- The tool creates an
mcp.jsonfile you’re ready to go!
Ask in English, Get KQL Insights Instantly
Once the server is running, you can start using the chat interface to query your data.
Examples:
- “List all my Kusto databases”
- “Summarize the data in ContosoSales”
- “What was the top selling product in 2008?”
The MCP Server:
- Understands context from your prior queries
- Analyzes database structure
- Pulls sample data and metadata
- Returns results with charts, KPIs, or schema insights
No KQL syntax. No guessing. Just real-time answers.
Real-World Benefits
- Non-technical users can explore data with ease
- Reduces training overhead on KQL
- Improves adoption of Microsoft Fabric Real-Time Intelligence
- Bridges the gap between natural language and technical querying
Final Thoughts: Is MCP Server the Future of Querying?
This is a big leap for anyone using Microsoft Fabric or Eventhouse. By lowering the barrier to KQL, the MCP Server enables faster decisions, wider adoption, and smoother experiences across your data team.
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