8 Things You Should Know About MCP in Microsoft Fabric

Artificial intelligence is evolving fast, but not just in how models generate responses. The next leap is agentic AI: intelligent agents that act autonomously, collaborate, and make decisions based on rich, evolving context.

But building systems that enable AI agents to interact meaningfully with data, apps, and each other? That's a massive challenge especially when relying on traditional APIs that weren't designed for dynamic, multi-agent workflows.

Enter the Model Context Protocol (MCP). A standard created by Anthropic and rapidly gaining support from Microsoft, OpenAI, Google, Databricks, Snowflake, and the broader analytics and AI community. MCP gives AI agents a shared language for context, enabling seamless cooperation and orchestration across platforms like Microsoft Fabric, Copilot Studio, and Azure AI Foundry.

If you're a CEO, innovation lead, strategy exec, or someone just wondering how to harness this next-generation AI stack, this article presents 8 essential things you need to know about MCP in Microsoft Fabric. But, before we cover those things, let's explore the problems MCP aims to solve.

Agentic AI and the Problem with Traditional APIs

The future of AI is about smarter actions. That's the promise of agentic AI: systems that can reason, collaborate, and operate autonomously across tasks and tools. Think of agents as digital colleagues who are capable of handling complex, multi-step workflows with minimal oversight.

The traditional way that AI agents interact with APIs

But there's a catch. Today's software systems rely on traditional APIs which are rigid, stateless, and designed for one-off requests. These APIs weren't built for agents that need to maintain context, adapt in real time, or coordinate across multiple services. This leads to broken workflows, duplicated effort, and context loss at every step.

This context problem is what makes agentic AI so difficult to scale in enterprise environments. Without a standard way to share, reuse, and evolve context, agents end up operating in silos; intelligent, but disconnected.

That's the gap MCP fills. It's not just a technical protocol; it's a reimagining of how systems and agents exchange meaning, and there are early explorations and prototypes within Microsoft's data and AI ecosystem.

What is MCP and Why It Matters

At the heart of agentic AI lies a simple but powerful need: context. Without it, even the smartest AI agents operate like amnesiacs, repeating tasks, misunderstanding instructions, and failing to collaborate. That's where MCP comes in.

MCP is an open standard that lets AI models and agents share structured context with each other persistently and meaningfully as well as tapping into a variety of knowledge sources. Developed by Anthropic and being actively integrated into Microsoft's data and AI ecosystem, MCP provides a common language that AI systems can use to describe who they are, what they're doing, what data they need, and how they're connected to broader workflows. MCP can also be complimented by another protocol called the Agent2Agent Protocol (A2A), which focuses on standard ways for agents to interact with each other. You can learn more about the A2A protocol here.

Think of it this way: if traditional APIs are like walkie-talkies that utilise short, one-off instructions, then MCP is like a shared team whiteboard that updates in real time, is visible to all agents, and is capable of carrying forward an evolving narrative of tasks, data, and goals.

In Microsoft Fabric, this protocol becomes especially powerful. Today, it empowers agents in Copilot Studio and Azure AI Foundry to understand and leverage Fabric data agents. Tomorrow, we can expect more. This means better coordination, more autonomy, and far less engineering overhead when it comes to maximising the reach and impact of data across your organisation.

How MCP simplifies AI agent to API interactions

MCP matters because it upgrades AI from isolated task runners to collaborative digital agents that can drive real outcomes across your organisation, and Fabric is quickly becoming the hub where all of that comes together.

The 8 Things You Should Know

Microsoft Fabric is evolving from a data platform into an intelligent AI ecosystem, and the Model Context Protocol (MCP) is a big reason why. MCP enables smarter, more collaborative AI agents that can act with context, speed, and purpose.

So here are eight things every tech-savvy leader should know about it.

1. MCP Gives AI Agents Shared Memory and Purpose

In traditional software systems, AI models and tools behave like isolated workers, each handling a specific task with no awareness of the larger picture. MCP changes that. It gives AI agents a shared, structured memory that includes their identity, objectives, and the data or tools they're using.

In Microsoft Fabric, this shared context and memory allows AI agents to collaborate intelligently, adapting their actions based on context. Rather than relying on hardcoded rules or one-off integrations, MCP allows agents to act with awareness–like members of a team working toward a common goal. This unlocks use cases that were previously too complex to automate effectively, from dynamic report generation to multi-step analytics workflows.

For decision-makers, it means better coordination across systems, smarter automation, and the ability to scale AI capabilities without increasing engineering overhead.

2. You can experiment with MCP-based patterns alongside Fabric's Real-Time Intelligence

Real-Time Intelligence (RTI) in Microsoft Fabric empowers businesses to quickly create end-to-end solutions that extract insights and trigger actions from streaming data, data logs workloads, and real-time events. By exploring MCP with RTI, you can work towards creating agentic solutions that would otherwise be unfeasible due to context gaps and system fragmentation.

Combined with RTI, MCP enables AI agents to receive streaming data, understand its significance, and act in real time, all while maintaining a shared context with other agents and services. For example, a supply chain agent can respond instantly to shipment delays, or a fraud detection agent can flag anomalies as they happen.

The key advantage isn't just speed; it's coherence as well. Because the agents are context-aware, their decisions are aligned with current business goals and the broader state of the system. This transforms real-time intelligence into a live, autonomous and intelligent decision-making engine, helping businesses stay proactive instead of reactive.

3. MCP + GraphQL Makes Agents Queryable Like Databases

Microsoft Fabric now supports the combination of GraphQL and MCP through an open-source MCP server implementation from Microsoft. With this, you can enable AI agents to query data in Fabric SQL databases, data warehouses, lakehouses, and mirrored databases through the Fabric GraphQL API. This means developers and analysts can instruct AI agents using prompts, requesting insights, triggering actions, or navigating context in real-time with no need for bespoke endpoints or custom integrations.

For example, a marketing analyst could query an agent for campaign performance filtered by region and channel, and receive summarised results shaped by shared context. This approach dramatically lowers the barrier to working with AI in enterprise environments, making it easier to integrate agents into dashboards, reporting tools, or even web apps.

The GraphQL layer gives MCP enabled agents the ability to plug into existing data workflows, bringing AI closer to everyday decision-making.

4. MCP Enables Multi-Agent Orchestration in Fabric and Copilot Studio

AI is becoming more powerful, but no single agent can handle everything. MCP enables Microsoft Fabric and Copilot Studio to be combined in multi-agent orchestrations, where agents specialise in different tasks but collaborate through a shared context. Imagine one agent summarising sales data, another generating recommendations, and a third updating visual dashboards, all seamlessly coordinated.

MCP, when combined with an agent to agent protocol (e.g. A2A), acts as the glue between them, ensuring context is maintained, goals are aligned, and handovers are frictionless. This orchestration model is far more adaptive than traditional automation, allowing AI systems to evolve organically as business needs change. This marks a shift from isolated automation to collaborative AI ecosystems that learn and improve over time.

Although Copilot Studio lets you define agents and workflows without code, it's worth noting that you will often need code or custom connectors when integrating with external MCP servers.

5. It's Easier Than Ever to Build AI-Driven Workflows in Fabric

Thanks to Microsoft's investment in open-source tools and community samples, getting started with MCP in Fabric is surprisingly approachable. The Microsoft Fabric samples repository on GitHub offers ready-to-use examples that show how to connect agents, build GraphQL queries, and structure contextual workflows using MCP. This can help teams move beyond theory and quickly start prototyping real-world applications.

Whether you're building a custom monitoring solution or enhancing a data lake with agentic AI, you don't need to reinvent the wheel. Microsoft is making it easy to plug into existing tooling so AI becomes a natural extension of your data workflows. For innovation teams and data leaders, this means faster experimentation, faster deployment, and a quicker path from idea to impact.

6. MCP Brings Intelligence to Infrastructure-as-Code Development for Fabric

Microsoft has provided guidance on how to combine the Microsoft Learn MCP server and Terraform MCP server in a way that can help your team generate Terraform templates and Fabric resource definitions. This enhancement to the Infrastructure-as-Code (IaC) development workflow reduces the number of time-consuming and tedious tasks. More advanced uses of MCP in this scenario could make it possible to generate initial versions of CI/CD pipelines, further reducing the time and effort associated with making IaC changes.

7. MCP Future-Proofs Your Fabric Investment for AI-First Workflows

As the AI landscape evolves, one of the biggest risks is getting locked into inflexible systems that can't adapt. By weaving MCP into Fabric's ever-growing data, analytics, and AI capabilities, Microsoft ensures that your investments today will support AI-native architectures of tomorrow.

Whether you're building a customer insights engine, an autonomous reporting system, or a multi-agent chatbot network, MCP allows you to scale without rewriting everything. It supports interoperability across services, easy integration with Azure and Power Platform, and compatibility with future model updates. This means your systems can grow smarter and more complex over time without becoming brittle.

MCP's presence in Fabric signals that Microsoft is betting on agentic AI as a long-term paradigm, giving your organisation the confidence to innovate without fear of obsolescence.

8. MCP Is Open, Extensible, and Evolving Rapidly

Unlike many proprietary AI orchestration technologies, MCP is an open protocol developed by Anthropic and actively supported by Microsoft. It is open-source, has publicly available documentation, growing GitHub projects, and a vibrant developer community. That openness makes it highly extensible; you can modify, integrate, or expand it to suit your unique needs.

It also means you're not locked into any one vendor or ecosystem. Microsoft's contribution to MCP's tooling (GraphQL bridges, Terraform integration, Copilot Studio support, and Azure AI Foundry Agent support) is evolving fast, and community examples are popping up across the web. This rapid evolution means your team benefits from a rising tide of innovation, without being left behind.

For enterprises aiming to build sustainable, future-ready AI systems, MCP is more than a protocol; it's a commitment to openness, scalability, and shared progress.

MCP marks a shift from rigid APIs to intelligent, context-aware, agentic solutions. With Microsoft deeply integrating it across Fabric, Copilot Studio, and beyond, the time to explore its potential is now. Embracing MCP means preparing your organisation for an AI-first future that's smarter, faster, and truly connected.

Conclusion

The Model Context Protocol (MCP) isn't just another technical standard; it represents a shift in how intelligent systems are built, deployed, and scaled. Traditional automation and AI models have long struggled with a critical limitation: the inability to maintain and share context. MCP solves this by providing a common language for AI agents to understand who they are, what they're doing, and how they fit into a larger objective.

With Microsoft Fabric embracing MCP across real-time intelligence, GraphQL workflows, Terraform IaC, and multi-agent orchestration, organisations now have the building blocks for truly adaptive, scalable AI systems. And it doesn't stop there. Copilot Studio and Azure AI Foundry are extending these capabilities even further, enabling teams to design AI agents that are not only smart, but also collaborative, situationally aware, and deeply integrated with the business.

For forward-thinking leaders, MCP unlocks the ability to create AI-native workflows that are flexible, future-proof, and aligned with enterprise goals. Whether you're looking to streamline analytics, automate decision-making, or build a new class of AI-driven applications, MCP gives you the foundation to do so intelligently and sustainably.

Future-Proof Your AI Strategy

AI is moving fast, so don't let outdated systems hold you back. With Microsoft Fabric and Copilot Studio, you can build adaptive, context-aware workflows that scale with your business. Ready to unlock next-gen intelligence? Learn about how we can help you use AI in real-world use cases. We'll guide your team from strategy to implementation so that your AI systems are not just smart, but enterprise-ready.