Ever wanted your AI agent to interact with Microsoft Fabric using plain English? That’s exactly what Model Context Protocol (MCP) servers enable.
What is MCP?
MCP is an open standard that gives AI agents a secure, unified way to access data and services—eliminating fragile custom integrations. It brings structured operations, discoverable capabilities, and a secure-by-design approach.
Two MCP servers, different needs:
🔹Fabric Core MCP Server (remote)
No setup required. API-based access with OAuth 2.0—ideal for quick, governed access to Fabric.
🔹Fabric MCP Server (local)
Open-source and developer-friendly. Provides API docs and local file system access—great for hands-on development.
Why it matters
These capabilities allow teams to interact with Fabric naturally using AI—accelerating productivity from prototyping to production.
🔒Security remains key, with support for authentication and role-based access control.
Whether you want speed or control, there’s an MCP server for your workflow.
👉Which approach works best for you—remote for simplicity, or local for flexibility?

#MicrosoftFabric #AI #MCP #DataEngineering #ModelContextProtocol #AgenticAI #Microsoft
Reference: https://learn.microsoft.com/en-us/rest/api/fabric/articles/mcp-servers/what-is-fabric-mcp-server 

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The SAP Procure-to-Pay (P2P) process sits at the heart of enterprise operations—connecting procurement, finance, suppliers, and compliance into a single continuous flow. Yet, despite its critical importance, most organizations still struggle with fragmented data, delayed insights, and limited visibility across structured transactions, unstructured policies, and real-time operational signals.

Stop managing tables and start architecting meaning. Discover how to use Microsoft Fabric IQ to transform fragmented agricultural data, from real-time IoT sensors to static financial reports into a unified, AI-ready Ontology. In this guide, I explore the 'Multi-Source Binding' strategy that allows your data to speak the language of your business finally.

How a semantic layer turns OneLake into your company’s executable vocabulary—and why that changes everything for your AI agents.

🚀MCP vs. Plugins vs. Skills for Agents: The Path to Native Agentic AI Development

Every truly useful AI agent must go beyond its training data.

To be effective in the real world, an agent needs to call APIs, query databases, operate infrastructure, understand domain‑specific workflows, and adhere to organizational standards and guardrails. The question is no longer whether agents should be extensible—it’s how that extensibility should be designed.

By 2026, three distinct extensibility models have crystallized across the agentic AI ecosystem:

Model Context Protocol (MCP) — a universal, open protocol that connects agents to external tools, services, and data sources
Plugins — installable, distributable packages that bundle tools, instructions, configurations, and sometimes sub‑agents
AI Skills — structured, reusable prompt artifacts that encode domain expertise and guide how agents reason, decide, and act
These models are often discussed as if they compete with one another. They don’t.

Each operates at a different layer of the agent stack, and the most capable agent architectures intentionally combine all three. The problem is that their roles are frequently misunderstood, which leads to poor design decisions, fragile agents, and unnecessary complexity.
In this post, I’ll break down what each model actually does, where it fits in the architecture, and how to compose MCPs, Plugins, and Skills into a cohesive, production‑ready agentic system—one that scales across teams, tools, and environments.

Blog :https://github.com/AmishSinhaMS/mcp-vs-plugins-vs-skills 

#mcp #plugins #extensibility #aiagents #githubcopilot #agentic-ai model-#context-protocol #ai-skills #microsoft-foundry #Skills for Fabric #Microsoft Fabric #CLI 

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Fundamentals are getting a re-write.  Fabric Data Agents mark a shift from humans authoring logic to systems that reason, going through governance, not around it. For years, we’ve optimized how humans interact with data. We have taught people how to write better queries, built semantic models to abstract business logic and added governance so analytics could scale safely.  We call those kinds of things fundamentals.  Well, fundamental is getting a rewrite, and the new fundamental is something much more like a reasoning surface.

Even when data is centralized, business meaning can still vary across teams.
This article explores why semantic alignment matters and how Microsoft Fabric IQ helps organizations unify not just their data, but the meaning behind it.

With terms like Work IQ, Fabric IQ, Foundry IQ, Agent 365, and Copilot Studio flying around, it’s easy to get lost in the noise. However, there is a clear architecture behind it all.
🔹Work IQ → User context (how people collaborate)
🔹Fabric IQ → Data context (structured business data)
🔹Foundry IQ → Knowledge context (enterprise policies & intelligence)
These aren’t products—they’re architectural layers that make AI useful at enterprise scale.

Leverage Microsoft Fabric IQ and Graph (preview) in solutions to combat financial fraud in Anti-money laundering (AML) use cases.

If you’ve ever felt buried in fragmented datasets and mismatched schemas, my latest post on Microsoft Fabric IQ might be your cup of tea. I walk through how I coaxed Fabric’s new Ontology item into weaving store sales, product catalogs and freezer telemetry into a single, live business graph—complete with a hands‑on setup guide and runnable code. It’s a mix of practical steps, personal missteps and a few laughs, all in an approachable 10‑minute read. Check it out to see how Fabric’s semantic layer can turn chaos into clarity