A more connected future with MCP

AI is evolving rapidly, and with it comes new ways to improve its efficiency and connectivity to real-time data. One of the most recent advances in this field is the Model Context Protocol (MCP), an open standard that allows AI models to directly access files, APIs, and tools without the need for intermediate processes.

Here, we explore what it is, how it works, and the keys to understanding why it could transform the future of AI.

MCP is an open protocol that standardizes how applications provide context to LLMs. We can imagine it as a USB-C port for AI applications, where it provides a standardized way to connect AI models to different data sources and tools.

The importance of the MCP lies in the fact that it helps to create complex agents and workflows on top of LLMs. As these LLMs often need integration with data and tools, the Model Control Protocol comes to offer:

• A growing list of pre-designed integrations that an LLM can connect to directly.
• The flexibility to switch between LLM vendors and providers.
• Best practices for protecting your data within an infrastructure.

In essence, MCP follows a client-server architecture where a host application can connect to multiple servers:

• MCP Hosts: programs such as Claude Desktops, IDE, or AI tools that want to access data via MCP.
• MCP Clients: protocol clients that maintain 1:1 connections with servers.
• MCP servers: lightweight programs that expose specific capabilities through the standardized protocol.
• Local data sources: the files, databases, and services on a computer that MCP servers can securely access.
• Remote services: external systems available through the Internet that MCP servers can connect to.

When a user makes a query, the AI assistant connects to an MCP server, which retrieves the information from the appropriate source and returns it without further processing.

As we have been saying throughout the article, implementing MCP in AI systems offers significant advantages compared to other data retrieval architectures such as RAG systems.

According to The Wired, the path to AGI is being rewritten, and where once the conversation revolved around “more data, more computation,” today the debate is opening up to the idea of networks of “nano-employees” or specialized mini-agents, capable of organizing themselves to solve complex problems. MCP is the key piece that allows these agents to communicate and share context, an essential condition for the emergence of a collective intelligence.

Some of its benefits are:

• Real-time access: AI models can query databases and APIs in real time, eliminating the problem of outdated responses or reliance on reindexing processes.
• Increased security and control: By not requiring intermediate data storage, MCP reduces the risk of leaks and ensures that sensitive information remains within the business or user environment.
• Reduced computational burden: eliminates the need to rely on embedding, resulting in lower costs and greater efficiency.
• Flexibility and scalability: allows connecting any AI model with different systems without requiring structural changes, which makes it a very good option for companies working with multiple platforms and databases.
• Accelerated experimentation: it is easier to test new use cases, which favors continuous innovation.

Microsoft has announced the first release of MCP support in Microsoft Copilot Studio, which aims to make it easy to add AI applications and agents to the platform with just a few clicks.

By connecting to an MCP server, actions and knowledge are automatically added to the agent and updated as functionality evolves. This simplifies the agent creation process and reduces the time spent on agent maintenance.

MCP servers are available to Copilot Studio via the connector infrastructure, which means you can implement enterprise governance and security controls, such as virtual network integration, data loss prevention controls, and multiple authentication methods, while supporting real-time data access for AI-enabled agents.

To get started with it, you need to access your agent in Copilot Studio, select “Add an action,” and search for the MCP server.

Each tool published by this server is automatically added as an action in Copilot Studio and inherits the name, description, inputs, and outputs.

As tools are updated or removed on the MCP server, Copilot Studio automatically reflects these changes, ensuring that users always have the latest versions and that obsolete tools are removed. This streamlined process not only reduces manual work but also reduces the risk of errors caused by obsolete tools.

It also includes Software Development Kit (SDK) support, allowing for greater customization and flexibility in integrations. To create your own MCP, follow these three key steps:

• Create the server: To integrate Copilot Studio with MCP, you need to create a server using one of the SDKs that will serve as the basis for managing the data, models, and interactions.
• Publish via a connector: Once the server is in place, the next step involves creating a custom connector that links your Copilot Studio environment to the model or data source.
• Consume the data through Copilot Studio: Once the server is set up and the connector is defined, you can start consuming the data and interacting with the models.

This creates an optimized and adaptable integration with Copilot Studio that not only connects systems but also enhances your ability to maintain and scale this integration according to your needs.

Ultimately, MCP represents a major shift in how AI models interact with real-time data. By eliminating the need for intermediate processes such as embeddings and vector databases, Model Context Protocol offers a more efficient, secure, and scalable solution.

Since the future of AI lies in the ability to adapt and respond with accurate, real-time information, MCP is poised to become the new connectivity standard for AI models.

At Plain Concepts, we’ve been AI experts for over a decade, and we’ve delivered hundreds of projects and results that have put our clients ahead of their competition. And now we can help you get there.

If you want to know more about MCP, don’t miss the talk given at the last edition of dotNET 2025 by our colleagues Jorge Cantón, Research Director, and Rodrigo Cabello, Principal AI Research Engineer, entitled “Model Context Protocol: Learn how to connect your services and applications with artificial intelligence”. In it, they will guide you through a brief introduction to the MCP, explaining how it works and why it is key to enable secure and controlled interactions between AI models and external environments. They will then share a practical example in which they expose how to turn custom APIs into accessible tools for AI, enabling the orchestration of advanced and personalized interactions between different language models (LLMs). And they close the session with an applied creative use case, where they show how AI can generate 3D environments in real time using the Evergine graphics engine, highlighting the great potential of this integration in interactive and visual scenarios. Coming soon on our website and YouTube channel, don’t miss it!