There’s a pattern we see again and again. A new technology arrives, everyone builds their own custom way of connecting to it, and very quickly nothing works the same way anymore. Eventually someone steps back and says, “We can’t keep doing this - we need one standard way to connect everything.”

That’s where the Model Context Protocol (MCP) comes in. Announced by Anthropic in November 2024, MCP is an open standard that gives AI assistants a single, consistent way to connect to enterprise data. For the first time, there’s a real answer to the big question every company is asking: “How do we connect our AI systems to all our data without building hundreds of custom integrations?”

And the timing makes sense. Companies have moved past small AI experiments and now want to use AI across the organisation. The models are strong. The use cases are real. But connecting AI to all the places where data lives - databases, CRMs, file shares, SharePoint, internal apps - quickly becomes the biggest problem. Each system needs a different connector. Each connector needs maintenance. Security teams get nervous about all these one-off solutions. And suddenly, the ROI everyone expected starts to fade.

MCP solves this by providing one common interface for all AI-to-data connections that implement MCP. It’s very similar to what Docker did for applications: before Docker, apps behaved differently on every server; after Docker, everything ran in a standard way. MCP brings that same simplicity to AI integrations - one standard approach that works no matter where the data lives.

Think of MCP as USB-C for AI systems.

Remember when every device had its own proprietary charging port? iPhones had their Lightning cable, Android phones used micro-USB, laptops had barrel connectors, and tablets had their own variants. If you travelled, you carried a bag full of cables. If you wanted to connect devices, you needed dongles and adapters for every combination.

USB-C changed this. One port, one cable, works with everything. Your phone, laptop, monitor, and headphones all use the same connection standard.

MCP does the same thing for AI systems connecting to data sources.

It's a standard protocol for connecting AI assistants to systems where data lives - databases, content repositories, business tools, and development environments. Instead of each AI application implementing its own way to talk to databases, file systems, and APIs, they all speak MCP. Instead of each data source creating custom endpoints for every AI tool, they expose a single MCP interface.

In practical terms, MCP defines just three simple things:

Just as USB-C removed the need for different chargers and cables for every device, MCP removes the need for different custom integrations between every AI system and every data source.

This isn’t some complicated new technology. It’s simply smart engineering - seeing a common problem, standardising it early, and preventing a messy, fragmented ecosystem from becoming permanent.

The problem MCP solves is something every enterprise architect has faced: too many integrations that all work differently.

Before MCP, connecting an AI system to enterprise data looked like this:

Every AI application and every data source needed its own custom connector. The numbers got out of hand quickly. If you had 10 AI apps and 20 data sources, that could mean 200 separate integrations to build, secure, and maintain.

Just keeping these working was a huge burden.
If an API changed, you had to update dozens of connectors.
Security patches had to be applied in many different places.
Documentation went out of date.
Only a few people understood how things worked.

Before MCP, there was no shared standard for connecting AI models to external systems. Every integration was one-off and incompatible with the next.

With MCP, this changes completely:

Now, with MCP, each data source only needs one MCP server implementation. And each AI application only needs one MCP client implementation.

All those hundreds of one-off integrations disappear.
You build something once, and it can connect to anything else that speaks MCP.

The “write once, connect anywhere” idea finally becomes real.

This is where MCP really shows its strength. The MCP server sits in the middle - between the standard protocol and the actual data source.

Because of this, the person building the MCP server can change anything behind the scenes: how they fetch data, how they authenticate, how they optimise queries, or even which backend system they use.

And the AI applications using it don’t need to change at all. They keep working exactly the same way.

Consider a practical scenario: your MCP server for a data store backed by SQL Server initially connects directly to the database. Later, you may decide to:

You can make all of these changes behind the scenes without touching the AI applications. They continue using the same MCP interface, so nothing breaks.

Without MCP, every one of these backend changes would force you to update every AI application that connects to your database. That means code changes, testing, and coordinating deployments across multiple teams. It quickly becomes slow and expensive.

With MCP, you only update the MCP server once. The standard interface stays the same. All AI clients keep working without any changes.

It follows the same idea that makes microservices work well: a stable interface that hides all the internal details. The MCP server becomes a clean abstraction layer, allowing your backend systems to evolve without breaking the applications that depend on them.

MCP is not just another integration protocol to evaluate. It’s becoming a foundational layer in the Azure AI ecosystem, and its influence is already visible across Microsoft’s cloud, developer, and AI platforms. For Azure architects, this is why MCP deserves immediate attention.

Microsoft now ships one of the most comprehensive MCP ecosystems in the industry.
The Azure MCP Server alone exposes tools for dozens of Azure service areas - including SQL Database, Cosmos DB, Blob Storage, Key Vault, Azure AI Search, Event Hubs, Service Bus, App Configuration, and Azure Monitor.

Beyond Azure services, Microsoft has released dedicated MCP servers for GitHub, Azure DevOps, SQL Server, Dev Box and Microsoft Learn Docs.

All of these follow a single authentication model and consistent interaction pattern, giving architects a uniform way to integrate the entire Microsoft estate without bespoke connectors.

The significance of MCP goes beyond Microsoft’s adoption.
Major platforms - Figma, Notion, Linear, Atlassian, Zapier, Stripe, PayPal, Square, MongoDB, Neon, and others - have all built MCP servers that follow the same protocol.

This creates something Azure architects have wanted for years:
a vendor-neutral integration standard.

It becomes possible to design AI workflows where Azure services, GitHub repositories, Notion documentation, Linear issue tracking, Stripe payments, and MongoDB data operations all work together without custom integration code.
This reduces ecosystem lock-in while maintaining consistent governance and security guardrails.

Agents in Azure AI Foundry (rebranded as Microsoft Foundry) can consume remote MCP servers as tools with minimal custom development.
Architects can construct agent workflows that span Azure services, third-party SaaS platforms, and enterprise systems simply by registering MCP endpoints.

Custom connectors eventually become technical debt. MCP changes this by letting you maintain one integration layer instead of many.

If authentication changes, you update the MCP server.
If a new data source is needed, you add it to MCP.
Your AI applications don’t need any modifications.

For Azure architects building AI-ready environments, MCP shouldn’t be optional — it belongs in the core reference architecture for enterprise-scale workloads.

MCP solves a problem every enterprise faces today: too many tools, too many custom integrations, and too many moving parts. By giving models and agents a standard way to connect to Azure services and third-party systems, it removes much of the complexity that slows AI adoption.

For Azure architects, MCP offers a predictable, secure, and future-ready integration layer. It reduces the need for bespoke connectors, fits naturally into existing governance, and makes it easier to build AI systems that can evolve without constant rework.

As the ecosystem grows, MCP is on track to become a core part of how AI applications are designed and connected on Azure. Adopting it early means cleaner architectures, less technical debt, and far more flexibility for the future.

If you found this useful, tap Subscribe at the bottom of the page to get future updates straight to your inbox.