What is MCP and When Should You Use It?

As AI systems become increasingly advanced, new challenges emerge around integration, scalability, and security. This is where the Model Context Protocol (MCP) has rapidly taken on a central role—comparable to how USB became the standard for hardware and HTTP for the web. But what exactly is MCP, why has it gained such popularity, and when is it the right choice for your AI project? In this article, you’ll get a thorough overview of MCP, practical use cases, and tips for getting the best results.

What is MCP? – Background and Core Concepts

Model Context Protocol (MCP) is an open protocol that enables AI models to communicate seamlessly with external tools, data sources, and systems. Instead of requiring custom integrations for each tool, MCP creates a unified standard—similar to USB for hardware or REST APIs for the web.

As of February 2025, there were over 1,000 community-built MCP servers (so-called connectors) and more than 5,000 public MCP servers available. The Python SDK for MCP was being downloaded over 6.6 million times per month. With forecasts suggesting that MCP will become as standardized as REST APIs before 2027, early adopters can benefit from:

Lower integration costs through reusable connectors
Faster rollout of new AI features across the organization
Reduced technical complexity and better compatibility between AI models and data sources

Why Use MCP? – Business Benefits and Value

Companies that have adopted MCP report significant efficiency gains. Industry statistics show that integration costs have dropped by 30%, and project lead times have been cut in half. This means organizations can scale AI solutions faster and launch new features across departments without needing to rebuild their entire infrastructure.

More efficient development: Instead of crafting each integration from scratch, MCP connectors can be reused across systems and models.
Increased revenue: One company saw a 20% boost in revenue in just one quarter thanks to faster delivery of AI-powered features.
Improved customer satisfaction: An e-commerce platform used MCP for product recommendations and halved response times, significantly increasing customer satisfaction.

Research indicates that even a 5% improvement in operational efficiency can lead to multimillion-dollar savings for large organizations. MCP enables these gains by reducing redundant work and simplifying the integration of new data sources and services.

How MCP Solves the N×M Problem

One of the biggest challenges in AI integration is the so-called N×M problem: when you have N AI models and M tools or data sources, you’d typically need N × M unique integrations. This quickly becomes unmanageable, especially in complex systems.

MCP addresses this by:

Standardizing communication between models and tools
Eliminating the need for custom integrations for every combination
Making it easy to add new models or data sources without affecting the rest of the system

Real-world example: A company implemented MCP in its customer support system. The result? 50% shorter response times and a 25% productivity increase among support agents. By using ready-made MCP connectors, they avoided rebuilding their entire AI architecture with each update.

When Should You Use MCP? – Ideal Use Cases

Despite MCP’s many advantages, it’s not always the right choice. Here are some scenarios where MCP is particularly effective:

Scalable AI solutions: When you want to add tools, data sources, or models without reworking your integration logic.
Multiple teams or departments: If various parts of your organization need access to different AI functions, but you want to avoid duplication of effort.
Security requirements: Since MCP is an open protocol, you can host your own MCP servers within your infrastructure, keeping sensitive data behind firewalls.
Developer efficiency: Tools like the MCP GitHub Connector and Semantic Kernel MCP let developers quickly connect AI to different codebases and workflows.

In some simpler cases—such as basic API calls or where context injection via other formats suffices—MCP might be overkill. Always conduct a needs assessment before choosing a protocol.

Best Practices and Security Tips for MCP

MCP enables powerful access to data and code, which demands strong security and governance.

Use explicit user consent: No AI model should access a tool without clear, informed consent from the user.
Host MCP servers internally: For sensitive applications, run MCP servers behind your company firewall and expose only necessary functionality.
Support multiple transport protocols: A production-ready MCP server should handle various transport types for maximum compatibility.
Implement robust discovery endpoints: Early MCP clients could fail silently without proper discovery. Ensure your server is properly configured.

By following these guidelines, you can get the most out of MCP while minimizing risk.

Conclusion: Step Into the MCP Ecosystem

MCP is rapidly becoming the de-facto standard for AI integration. With thousands of community-built connectors, proven efficiency gains, and easier scalability, it’s clear why so many organizations are already on board. Start by assessing your current integration needs and identify where MCP can deliver the most value. With the right implementation, you can cut costs, accelerate innovation, and future-proof your AI infrastructure.

Ready to explore MCP? Dive into the documentation, test a connector, and discover how your AI project can reach the next level.

This article was assisted by AI.

What is MCP and When Should You Use It?