{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/11"}],"enrichment":{"capability":"An MCP server that bridges AI agents to Microsoft Fabric Real-Time Intelligence services, exposing Kusto queries, Eventstreams, Activators, and Maps as tools through the Model Context Protocol.","skillfed_tags":["mcp-server","fabric-rti","kusto-query"],"use_cases":["Enable AI agents to query Eventhouse data and perform trend analysis on historical datasets through natural language prompts.","Automate real-time alerting by creating Activator triggers that notify teams via email or Teams when KQL-monitored conditions occur.","Build and manage Eventstreams programmatically to ingest and process sensor or IoT data in real time.","Generate execution plans and compare query efficiency before running expensive Kusto queries against large datasets.","Create and manage geospatial visualizations on Maps by connecting Lakehouse data sources through the MCP interface."],"what_it_does":"This package implements a Model Context Protocol (MCP) server that acts as a bridge between AI agents and Microsoft Fabric Real-Time Intelligence services. It exposes a comprehensive set of tools for querying Kusto databases, managing Eventstreams, creating Activator triggers, and visualizing geospatial data through Maps. The server translates natural language requests from AI agents into KQL queries and Fabric API calls, handling authentication via Azure Identity.\n\nThe package includes 13+ Kusto tools for query execution and diagnostics, 17 Eventstream tools for creation and management, 2 Activator tools for alert setup, and 7 Map tools for visualization. It also bundles a KQL Copilot Skill that teaches agents about Kusto syntax, query patterns, and optimization. The package is in Public Preview and targets Python 3.10+, with dependencies on httpx, fastmcp, azure-kusto-data, azure-identity, azure-kusto-ingest, and msal.","worth_installing":"Yes, with conditions. The package is actively maintained and offers low install friction, but its Pre-Alpha status and explicit note that implementation may significantly change before General Availability mean it is best suited for exploration, prototyping, or integration into AI agent systems where breaking changes are acceptable. Production deployments should monitor the repository for stability signals and version milestones."},"id":"microsoft-fabric-rti-mcp","links":{"html":"https://skillfed.io/packages/microsoft-fabric-rti-mcp","md":"https://skillfed.io/packages/microsoft-fabric-rti-mcp.md","pypi":"https://pypi.org/project/microsoft-fabric-rti-mcp/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-16","license_spdx":"MIT","license_treatment":"permissive","name":"microsoft-fabric-rti-mcp","python_support":"supports_current","summary":"Microsoft Fabric RTI MCP"},"popularity":{"monthly_downloads":75208,"position":14740,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.6.2"}