Fabric Rti Mcp

🎯 Overview

A comprehensive Model Context Protocol (MCP) server implementation for Microsoft Fabric Real-Time Intelligence (RTI). This server enables AI agents to interact with Fabric RTI services by providing tools through the MCP interface, allowing for seamless data querying, analysis, and streaming capabilities.

[!NOTE]
This project is in Public Preview and implementation may significantly change prior to General Availability.

🔍 How It Works

The Fabric RTI MCP Server acts as a bridge between AI agents and Microsoft Fabric RTI services:

• 🔄 MCP Protocol: Uses the Model Context Protocol to expose Fabric RTI capabilities as tools
• 🏗️ Natural Language to KQL: AI agents can translate natural language requests into KQL queries and Eventstream management
• 💡 Secure Authentication: Leverages Azure Identity for seamless, secure access to your resources
• ⚡ Real-time Data Access: Direct connection to Eventhouse and Eventstreams for live data analysis
• 📊 Unified Interface: For both analytics and streaming workloads with intelligent parameter suggestions

✨ Supported Services

Eventhouse (Kusto): Execute KQL queries against Microsoft Fabric RTI Eventhouse and Azure Data Explorer (ADX).

Eventstreams: Manage Microsoft Fabric Eventstreams for real-time data processing:

• List Eventstreams in workspaces
• Get Eventstream details and definitions
• Create new Eventstreams
• Update existing Eventstreams
• Delete Eventstreams

Activator: Create and manage Microsoft Fabric Activator triggers for real-time alerting:

• Create new triggers with KQL source monitoring
• Set up email and Teams notifications when a condition occurs
• List Activator artifacts in workspaces

Map: Create and manage Microsoft Fabric Map to visualize geospatial data:

• Create a new map from a provided configuration
• Visualize data on maps
• List Map items in workspaces
• Delete Map items

🧠 Copilot Skills

This repository includes a KQL Copilot Skill (.github/skills/kql/) that gives AI agents deep KQL expertise when writing, debugging, or reviewing Kusto queries. The skill covers:

• Syntax gotchas and self-correction patterns for common KQL errors
• Dynamic type discipline, join patterns, datetime pitfalls
• Memory-safe query patterns and result-size discipline
• Advanced functions: graph queries, vector similarity, geospatial operations, time series
• Query templates for deduplication, top-N, sessionization, pivoting, and more
• Full error-to-fix mapping for rapid recovery

The skill references the Fabric RTI MCP tools (kusto_query, kusto_command, kusto_sample_entity, etc.) so agents know how to execute queries through this MCP server.

🚧 Coming soon

• Other RTI items

🔍 Example Prompts

Eventhouse Analytics:

• "Get databases in my Eventhouse"
• "Sample 10 rows from table 'StormEvents' in Eventhouse"
• "What can you tell me about StormEvents data?"
• "Analyze the StormEvents to come up with trend analysis across past 10 years of data"
• "Analyze the commands in 'CommandExecution' table and categorize them as low/medium/high risks"
• "Before running this query, check

... View full README on GitHub