The AI Agent's Secret Weapon: Introducing the Model Context Protocol (MCP)

AI is already transforming the way we work, but there’s still a missing link. Businesses run on a patchwork of systems, apps, and databases, and most AI agents struggle to make sense of it all. They can be powerful, but not always practical. 

So how do you get from potential to real impact? How do you make AI truly useful in the complex world of enterprise software? 

That’s where the Model Context Protocol (MCP) comes in. 

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What is MCP? 

Developed by Anthropic, MCP is an open standard designed to connect AI assistants directly to the systems where data resides, be it content repositories, business tools, or development environments. Think of MCP as the "USB-C of AI applications," providing a universal interface that allows AI models to access and interact with diverse data sources and tools. 

How MCP Works 

At its core, MCP standardizes the way AI models communicate with external systems. It comprises three main components: 

• MCP Client: The AI agent or application seeking to perform tasks or retrieve data. 

• MCP Server: The system or database that exposes its capabilities through standardized APIs. 

• MCP Host: The environment facilitating the interaction between clients and servers. 

By establishing a common protocol, MCP enables AI agents to discover and invoke APIs hosted on MCP servers, allowing for consistent and reliable interactions across various resources. 

This illustration was shared by Germán Huertas Piquero in his article on LinkedIn. It's a metaphor of a USB hub to represent how MCP acts as a connector between AI agents and various enterprise systems.

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How Promtior Is Using MCP 

One practical example of MCP in action comes from our work at Promtior with one of our clients. They needed a way to manage and consult tasks within their project management tool, Azure DevOps. To solve this, we identified and customized an MCP that enables our AI agent to interact directly with their Azure environment through the official API. This allowed them to streamline task handling and perform queries in a conversational manner, unlocking a smarter more integrated workflow across their systems. 

Implementing MCP translates into tangible benefits for enterprises: 

• Enhanced Efficiency: AI agents can perform tasks faster by accessing data directly, reducing the need for manual intervention. 

• Cost Savings: Standardized integrations minimize the need for custom development, lowering operational costs. 

• Scalability: MCP's cloud-native architecture ensures it can adapt to increasing workloads and evolving business needs. 

Moreover, MCP supports a repository of pre-built enterprise skills and recipes, enabling agents to perform complex tasks like data integration and process automation with ease. 

Industry Adoption and Future Outlook 

Since its introduction in November 2024, MCP has gained significant traction in the AI community. Major players like OpenAI and Google DeepMind have embraced the protocol, integrating it into their platforms to enhance AI capabilities. This widespread adoption underscores MCP's potential to become a universal standard for AI system connectivity and interoperability. 

Why MCP Matters Now

The Model Context Protocol is quietly reshaping how AI integrates into business operations. By creating a standardized, collaborative, and efficient framework, it allows AI agents to work directly with the systems your teams already rely on—CRMs like Salesforce, tools like Azure DevOps, platforms like SAP.

At Promtior, we’ve seen this firsthand. With the client we mentioned, for example, we implemented an MCP-based solution that connects their internal AI assistant with Azure DevOps—automating task queries and helping teams save valuable time.

MCP is already being adopted across the industry by companies focused on real, scalable AI use.

If you're exploring how AI can help your teams work smarter and faster, this is a practical, proven place to start. Let us know!