MCP: What It Is and Why It Matters

AI’s immense transformative potential may be undisputed, but it has a major problem: It is inherently difficult to integrate with real-world tools and systems like databases, calendars, CRMs, and financial platforms. As a result, developers are often stuck manually building custom integrations—an approach that’s both cumbersome and time-consuming.

No surprise then that a May 22, 2025, Gartner research report (https://www.gartner.com/en/newsroom/press-releases/2025-05-22-gartner-survey-finds-77-percent-of-engineering-leaders-identify-ai-integration-in-apps-as-a-major-challenge) reveals that 77% of software engineering leaders identify building AI capabilities into applications as a pain point.

A Forbes article by Jameel Francis (https://www.forbes.com/councils/forbestechcouncil/2024/11/15/why-85-of-your-ai-models-may-fail/) predicts that 85% of companies will struggle to integrate AI successfully, hindered by issues such as poor data quality, missing omnichannel integration, and continuous maintenance headaches. More recently, Storyblok commissioned a survey among senior developers that found a significant 58% are considering quitting their jobs due to inadequate legacy architecture, with, rather tellingly, 31% citing incompatibilities with innovation, such as AI, as a key reason (https://www.storyblok.com/mp/devbarrassment-survey).

 But while these statistics may paint a grim picture, the good news is that there is hope on the horizon. Enter the Model Context Protocol (MCP), a new standard designed to give developers better control over how AI systems interpret inputs across different environments and diverse use cases. Billed as a game changer for AI integration, it is quickly gaining traction among major players including Microsoft, OpenAI, and Google. But what exactly is it, and why should developers and businesses care?

WHAT IS MCP AND WHY DOES IT MATTER?

MCP is an open protocol that provides a standardized way of giving AI models the context they need. Think of it as the missing link that gives AI the context it needs to connect all the dots across the entire tech stack.

This matters because context is everything in AI-powered interactions. Whether you’re building an app, a chatbot, or an ecommerce engine, a model’s effectiveness depends on how well it understands the user’s intent, history, preferences, and environment. Traditionally, this context has been delivered through static prompts—a process that’s manual, timely, and difficult to scale.

MCP changes this. By standardizing how context—such as user roles, data sources, system states, and business logic—is passed to models, it ensures that AI no longer operates in a vacuum. Instead, it becomes environment-aware, unlocking more accurate outputs, smarter decisions, and seamless integration with tools such as CRMs, databases, productivity platforms, and financial systems. This means that rather than relying on scattered prompts, developers can now define and deliver context dynamically.

This makes AI integrations faster to build, easier to maintain, and far more robust. By decoupling context from prompt engineering and managing it like any other application layer, developers can effectively create their own structured, multilayered prompt interface. The upshot is that AI evolves from a black box into a transparent, deeply integrated part of your tech stack.

GETTING STARTED WITH MCP

The best part of all of this is that MCP is relatively easy to adopt, especially for developers familiar with APIs and modern app architecture. No deep AI expertise is required.

Start by identifying the core context elements your AI model needs in order to deliver accurate and relevant responses— think user roles, session data, system states, and business logic. Make sure these datapoints are well-structured, consistently maintained, and easily accessible within your application stack. Since MCP is all about delivering the right context at the right time, understanding where and how AI fits into your user experience is key.

Because MCP is API-first, you can begin experimenting with context-aware AI using the languages, tools, and frameworks you’re already comfortable with. Most developers can get a basic integration up and running in less than an hour.

As you scale, aim to integrate MCP gradually into your existing workflows. Run real-world tests to observe how different context signals shape model behavior. And, most importantly, treat context as a dynamic layer of your system— something to monitor, refine, and evolve based on how users interact with your product.

COMMON MISTAKES TO AVOID

As with any exciting disruption, the opportunity offered by MCP comes with its own set of challenges. Chief among them is poorly defined context. One of the most common mistakes is hardcoding static values. Instead, context should be dynamic and reflect real-time system states. Overloading the model with too much, too little, or irrelevant data is another pitfall and often leads to degraded performance and unpredictable outputs.

Failure to properly secure sensitive context information can also open the door to privacy and compliance risks, so it’s crucial to always enforce strong access controls and data protections. Ultimately, the effectiveness of any AI model using MCP hinges on the quality, clarity, and relevance of the context it receives.

Treating the MCP as a plug-and-play solution without tailoring it to your application’s unique domain is another common pitfall. While MCP is built for flexibility and modularity, getting the most out of it relies on carefully structuring context to fit your specific use case.

WHAT’S NEXT FOR MCP?

It may be early days for MCP, but its potential impact on AI integration is undeniable. In the next year or so, we can expect to see broader MCP adoption across industries, empowering developers to build smarter, more context-aware applications with less effort and greater reliability. In the not-too-distant future, the consensus too is that MCP could evolve to support increasingly complex data types and multimodal outputs, unlocking new possibilities in areas such as IoT, augmented reality, and collaborative AI.

Clearly, the move to MCP is less a case of “if” but “when.” For developers and businesses then, there may be no time like the present to start exploring MCP and unlock its powerful benefits for building smarter, more seamless AI integrations.