MCP: what is it and why should you care?

Artificial intelligence has moved at breakneck speed in recent years. What started with chatbots that could answer questions in natural language has evolved into tools that can generate code, write marketing copy, analyse data and even help manage projects. Yet for all this progress there’s been a nagging problem: how do we reliably connect these powerful models to the tools and services businesses already use?

Enter MCP- the Model Context Protocol. Announced by Anthropic in late 2024 and quickly embraced by other industry heavyweights including OpenAI, Google DeepMind and Microsoft MCP is being hailed as a turning point in the evolution of AI.

But what is it? Why does it matter? And what should business and technology leaders be doing about it?

In this article we’ll explore:

  • What MCP actually is (in plain English)

  • Why it’s being compared to USB-C for AI

  • How it changes the way AI interacts with your business systems

  • What benefits it brings to developers, businesses and end-users

  • Risks and challenges that still need attention

  • Why now is the right moment to pay attention

By the end you’ll see why MCP is far more than just another technical acronym - and why it could be the missing piece that takes AI from clever toy to serious business tool.

The integration problem: why MCP exists

Imagine you buy a new laptop. You want to connect it to a monitor, charge it and plug in a hard drive. A decade ago, you might have needed three different ports and three different cables - VGA for the monitor, a chunky barrel charger and USB-A for the drive.

It worked, but it was clunky, inconsistent and expensive.

Now think about AI. For years, large language models (LLMs) have been impressive conversationalists but relatively isolated. To make them useful in business contexts developers have had to build custom connectors - bespoke integrations between a given model (say GPT-4) and a tool (say Salesforce, Jira, or your internal knowledge base).

That leads to what engineers call the N×M problem: if there are N different models and M different tools, you need N×M connectors. The result is duplication, fragility and a nightmare to maintain.

MCP solves this by standardising the way models and tools talk to each other. Instead of bespoke connections both sides can implement the protocol once and interoperate with any compliant partner.

Just as USB-C means one cable for power, data and displays, MCP means one standard for AI-to-tool connectivity.

What exactly is MCP?

At its heart, MCP is a communication standard. It defines how an AI system (the “client”) can safely and consistently interact with an external tool, service or data source (the “server”).

The protocol specifies:

  • How requests are formatted - e.g. an AI asking for data from a database or instructing a system to trigger a workflow

  • How responses are returned - including structured data that models can reliably parse

  • How errors and permissions are handled - avoiding miscommunication or unsafe actions

  • Transport mechanisms - currently JSON-RPC 2.0 over HTTP or standard input/output

In practice that means:

  • A developer can expose their tool (say a payment API or a content management system) via an MCP server.

  • Any AI model that acts as an MCP client can then discover and interact with that tool - without a custom integration.

The protocol is open source and backed by SDKs in multiple languages (Python, TypeScript, Java, C# and more). Anthropic has also published reference servers for common services like Postgres, Stripe, Google Drive, and GitHub - making it easier for developers to get started.

A common way of linking applications

Why MCP is a big deal

So far, so technical. But why is this making waves across the AI and software industries?

1. It unifies a fragmented landscape

Before MCP every major AI provider was experimenting with its own “plugin” or “extension” ecosystem. OpenAI had ChatGPT plugins. Anthropic had “tools”. Microsoft embedded Copilot connectors. Each worked differently.

MCP offers a shared foundation. Instead of competing silos there’s now a path towards interoperability. That means less wasted effort for developers and more consistent experiences for users.

2. It turns AI from passive to active

A chatbot that can only talk is entertaining but limited. A model that can act - querying databases, sending emails, creating tickets, processing payments - is a genuine business partner.

MCP provides the plumbing to make this action-taking safe, reliable and scalable.

3. It reduces developer overhead

Instead of writing and maintaining one-off connectors developers can:

  • Implement MCP once for their tool

  • Know it will work with any compliant model

  • Focus their energy on features not glue code

That’s a massive efficiency gain.

4. It’s backed by the big players

Momentum matters. Within months of its launch MCP gained support not only from Anthropic but also OpenAI, Google DeepMind (integrating it into Gemini), Microsoft, Replit, Sourcegraph and others.

When rivals agree on a standard it’s usually a sign it’s here to stay.

Real-world examples: what MCP enables

To make this less abstract, here are some practical scenarios MCP could power:

  • Customer service: an AI agent connected via MCP to your CRM can not only draft a reply but also check the customer’s order history and issue a refund.

  • Software development: an AI coding assistant in your IDE can, through MCP, open GitHub pull requests, run tests in your CI system and update documentation.

  • Finance: an AI model can query real-time payment data from Stripe, reconcile transactions in your ERP and generate compliance reports.

  • Knowledge management: instead of copy-pasting content into a chatbot, an AI connected to your internal knowledge base via MCP can answer questions directly with source citations.

The business benefits

For organisations exploring AI adoption MCP brings several concrete advantages:

  1. Speed to value - connect once, integrate everywhere. Faster prototyping and rollouts.

  2. Lower costs - less custom engineering required.

  3. Future-proofing - a standard embraced by the biggest AI labs is less likely to vanish overnight.

  4. Consistency - a common integration layer avoids the chaos of multiple overlapping plugin systems.

  5. Empowerment - business users can benefit from AI that actually executes, not just advises.

Risks and open questions

No standard is perfect and MCP is still in its early days. Some challenges include:

  • Security: how do you prevent malicious instructions or tools from being exploited? Researchers have flagged risks like prompt injection, permission abuse or malicious server substitution.

  • Governance: who decides what “good behaviour” looks like across MCP servers and clients? Standards bodies may need to step in.

  • Complexity: while MCP reduces integration headaches, it doesn’t eliminate the need for careful design. Businesses still need to set guardrails.

  • Adoption curve: for MCP to truly succeed it must move beyond AI labs and developer tools into enterprise software vendors and SaaS platforms.

Why should you care now?

You might be thinking: “This all sounds interesting, but do I really need to care today?”

Here’s why the answer is yes:

  1. AI adoption is accelerating - the difference between “toy” and “tool” often comes down to integration. MCP is the enabler.

  2. Ecosystem momentum is building - early movers are already embedding MCP into their products. Waiting too long could mean playing catch-up.

  3. Strategic planning matters - even if you’re not deploying MCP tomorrow, understanding it now helps you future-proof your architecture.

  4. Opportunities for differentiation - businesses that embrace MCP early can design more powerful, more useful AI-driven experiences for customers and staff.

A common communication standard

How to get started

For business leaders:

  • Ask your vendors whether they are adopting MCP in their AI roadmaps.

  • Encourage your teams to explore MCP servers for internal systems—databases, document stores, CRMs.

  • Think strategically about where AI execution (not just conversation) could transform workflows.

For developers:

  • Explore the official MCP GitHub repository and SDKs.

  • Try out the reference servers (Postgres, Stripe, GitHub, Google Drive) to understand the patterns.

  • Consider exposing internal APIs via MCP to make them “AI-ready”.

The bigger picture: AI as an operating system

One way to think about MCP is as part of a bigger trend: AI models are starting to behave less like “apps” and more like operating systems.

Just as Windows or macOS orchestrates interactions between hardware, applications and users, LLMs equipped with MCP may soon orchestrate workflows across your digital estate.

That’s a profound shift. It means AI won’t just sit at the edges of your business - it could become the connective tissue holding disparate systems together.

Conclusion

The Model Context Protocol may sound like an obscure technical standard. But history shows that such standards often unlock the real power of new technologies.

  • USB made computers practical for everyday users.

  • HTTP made the web a universal medium.

  • SQL made databases accessible and consistent.

MCP could do the same for AI - turning isolated models into integrated, action-taking colleagues.

It’s not a silver bullet. Security, governance and adoption hurdles remain. But the momentum is undeniable and the implications are enormous.

So whether you’re a developer, a CIO or a business leader simply trying to understand where AI is heading, MCP is something you should care about.

Not because it’s a neat technical trick - but because it may well define how AI becomes truly useful in your organisation.