What is MCP and why should you know about it for your AI agents?
In this article, you’ll learn all about the Model Context Protocol (MCP), a protocol that’s changing the way AI models access external data. Unlike other methods, MCP creates an open standard that allows your AI agents to connect directly to files, APIs, and tools without additional steps.
Key Points
MCP (Model Context Protocol) revolutionizes how AI models access external data, offering significant advantages over traditional systems like RAG and establishing itself as the new industry standard.
• MCP accesses real-time data without prior indexing, ensuring more accurate and up-to-date information than traditional RAG systems.
• Significantly reduces computational load by eliminating the need for embeddings and vector searches, resulting in lower operating costs.
• Improves security by design by not requiring buffering, keeping sensitive data within the business environment.
• Simplifies integration architecture by reducing M×N to M+N connections, facilitating modular scalability with fewer integrations.
• It has mass adoption from tech giants such as OpenAI, Google DeepMind, and Microsoft, consolidating itself as a de facto standard by 2025.
MCP’s client-server architecture acts as a “USB-C port for AI applications,” enabling transformative use cases from process automation to natural language data analysis , positioning itself as a critical tool for maximizing the value of enterprise AI deployments.
How MCP differs from traditional RAG systems
Prior to MCP, most projects used Retrieval-Augmented Generation (RAG) systems to give context to AI models. We will show you the main differences between the two approaches:
RAG systems need to generate embeddings and save documents to vector databases. MCP works differently: it accesses your data directly without prior indexing, ensuring that the information is more accurate and up-to-date.
In addition, MCP reduces the computational load. While RAG relies on resource-intensive embeddings and vector searches, MCP eliminates this need. This translates into lower costs and greater efficiency for your projects.
Remember that in terms of security, MCP does not require intermediate data storage, reducing the risk of leaks and keeping your sensitive information within your business environment.
What role MCP plays in your AI models
The Model Context Protocol functions as a “USB-C port for AI applications,” providing a standard way to connect your models with different data sources and tools. This protocol uses a client-server architecture with three components:
- MCP Hosts: Applications that request information from an MCP server (such as AI wizards)
- MCP Clients: Manage the communication between the host and the server
- MCP Servers: Programs that expose functionalities to access files, databases and APIs
This structure allows your AI models to query and retrieve information in real-time without additional processing, facilitating effective communication between systems.
The adoption of MCPs by major technology companies
MCP was initially developed by Anthropic, creators of Claude, and became open source at the end of 2024. Since then, it has quickly gained acceptance in the industry.
OpenAI announced in March 2025 the adoption of the protocol as a recommended method to provide context and tools to its LLMs through its official Agents SDK. Google DeepMind confirmed in April 2025 its support for the standard, indicating that its Gemini models would be compatible with MCP.
Microsoft also supports the protocol with an official C# SDK for MCP, integrating it with Microsoft Semantic Kernel and Azure OpenAI. This massive adoption by major players cements MCP as a de facto standard, promoting interoperability between different language model vendors.
Technical architecture of the MCP protocol
In this section, you will learn how the architecture of the MCP protocol works. This system acts as a bridge that connects AI models with external resources, following a client-server model designed to facilitate the seamless exchange of information and capabilities.
MCP Host: The Starting Point of the Request
The Host MCP represents the AI-powered application that initiates the communication process. You’ll find that these hosts can be AI tools, code editors, or other software that seeks to improve their models using contextual resources.
A clear example is GitHub Copilot in Visual Studio Code, which acts as an MCP host and uses clients and servers to expand its capabilities. Remember that the host is the point where users interact with the AI and where the need for external data originates.
MCP Client: Message Translation and Transport
Below, we will explain the role of MCP customers. These components are used by the host application to establish connections to the servers. Each client maintains a one-to-one relationship with a specific server.
Its primary function is to translate the host’s requests into a format that the server can understand, and then to transport these messages. In addition, they manage two-way communication, ensuring that information flows correctly between the host and the server.
MCP Server: Connecting to Data Sources
MCP servers are services that expose specific functionality to clients. You’ll see how these servers provide an abstraction on top of REST APIs, local data sources, or other systems to provide business data to the AI model.
A server can connect to both local sources (internal files or services) and remote services via a network (web APIs or cloud platforms). This versatility allows you to adapt the system to different business needs.
Transport: JSON-RPC over HTTP or stdio
Client-server message exchange uses JSON-RPC 2.0, a lightweight remote procedure call protocol. MCP supports two main methods of transport:
- Standard input/output (stdio) for local communications
- HTTP with Server-Sent Events (SSE) for remote connections
This flexibility allows MCP to operate efficiently in both on-premises and distributed environments, adapting to different needs and deployment scenarios that you may require in your organization.
Why MCP Outperforms Traditional Systems
Have you ever wondered what makes MCP so different from other AI systems? When you compare MCP to traditional integration methods, you’ll discover significant advantages that can completely change how your AI projects work.
Direct access to your data without waiting
Unlike RAG (Retrieval-Augmented Generation) systems, which need to index and store documents in vector databases, MCP directly accesses the data without prior indexing. This ensures you get more accurate and up-to-date information.
Best of all, you can get data almost instantly, without intermediate processes. Your models query databases and APIs in real-time, eliminating those outdated responses that are so annoying when you rely on reindexing processes.
Fewer resources, more efficiency for your project
Here comes an advantage that you will immediately notice in your costs: the reduction of the computational load. While RAG systems consume significant resources with embeddings and vector lookups, MCP eliminates this need.
This optimization directly translates into lower operating costs and greater efficiency in data processing. If you work with applications that require immediate responses or operate with limited resources, this difference will be especially valuable.
Enhanced security for your sensitive data
In terms of security, MCP offers you a fundamental advantage. By not requiring intermediate data storage, you significantly reduce the risk of leaks.
This approach ensures that your sensitive information remains within your business environment. If you handle sensitive data or need to comply with privacy regulations, this feature will be critical for your organization.
Simple architecture that grows with you
Finally, MCP dramatically simplifies your integration architecture. The traditional problem required M×N connections (where M are the models and N are the data sources), generating exponential complexity. MCP reduces these connections to M+N.
This simplification allows you to connect new tools without writing repetitive code, focusing on building better functionalities. The modular structure makes it easy to adapt to different platforms and databases, minimizing friction points when integrating AI models with real-time data.
Real use cases of MCP in enterprises
Wondering how you can apply MCP in your company? Real-world implementations of this protocol are changing the way organizations use artificial intelligence in their day-to-day operations. From automation to data analysis, MCP allows you to contextually and efficiently access multiple sources of information.
Automating internal processes with AI agents
MCP makes it easy to automate repetitive tasks using intelligent agents. For example, you can implement an automatic invoice classification system that, through natural language, organizes documents by supplier. This approach allows processes such as expense accounting to be carried out without human intervention, connecting directly to your business databases.
Remember that this type of automation does not require advanced technical knowledge. The protocol is responsible for managing connections and the flow of information in a transparent way.
Customer support with access to real-time data
Do you want to improve your customers’ experience? In customer service environments, MCP allows virtual assistants to access multiple internal data sources such as corporate wikis, ERP systems, CRM, or technical documentation. This eliminates generic answers and allows for customized solutions based on up-to-date information.
The result is a significant improvement in the user experience, as they can get specific and up-to-date answers about their queries.
Natural language reporting and analysis
MCP transforms how you interact with enterprise data. With simple queries such as “cumulative sales this quarter by region,” the protocol formulates the query to the data warehouse and presents results in visual formats. This capability democratizes access to critical information without the need for technical knowledge.
We recommend this implementation if your team needs frequent access to business data to make decisions.
MCP for ChatGPT and custom assistants
MCP’s integration with ChatGPT and other custom assistants allows for more contextualized experiences to be created. Microsoft has implemented MCP support in Copilot Studio, making it easy to add AI applications and agents with just a few clicks. This simplifies the creation and maintenance of business wizards while ensuring proper security and governance controls.
If you need help implementing MCP in your systems, our development team can help you integrate the protocol with your existing business tools.
Conclusion
In this article, you’ve learned how the MCP protocol can significantly empower your AI projects. We have explored how this open standard allows AI agents to directly access files, APIs, and tools without intermediate processes, offering considerable advantages over traditional systems such as RAG.
MCP’s client-server architecture offers you concrete benefits for your deployments:
Step 1: Real-time access without prior indexing ensures more accurate and up-to-date information for your applications.
Step 2: The lower computational load translates into reduced costs and greater operational efficiency in your systems.
Step 3: Enhanced security eliminates the need for intermediate storage, protecting sensitive data within your business environment.
Remember that adoption by tech giants such as Anthropic, OpenAI, Google DeepMind, and Microsoft cements MCP as an industry standard. This acceptance ensures interoperability between different suppliers, facilitating implementation in your projects.
The case studies we’ve reviewed demonstrate the potential of MCP in real-world scenarios: internal process automation, personalized customer service, and natural language data analysis. These apps show you how you can significantly improve the efficiency and accuracy of your AI-based systems.
We recommend you consider MCP for your next AI projects. The simplicity of its architecture, coupled with its ability to reduce integration complexity, positions this protocol as a critical tool for maximizing the value of your AI deployments.
If you have any questions about implementing MCP in your projects, specialized technical support can help you take full advantage of this protocol.
FAQs
Q1. What exactly is the MCP protocol and how does it work?
The Model Context Protocol (MCP) is an open standard that allows AI models to directly access external data, APIs, and tools without intermediate processes. It works through a client-server architecture that facilitates communication between AI models and various sources of information in real time.
Q2. What are the main advantages of MCP compared to traditional systems such as RAG?
MCP offers access to real-time data without prior indexing, lower computational load, greater security by not requiring buffer storage, and a simpler architecture that facilitates scalability. This translates into more accurate information, reduced costs, and better protection of sensitive data.
Q3. Which major companies have adopted the MCP protocol?
Big tech companies like Anthropic (creators of Claude), OpenAI, Google DeepMind, and Microsoft have adopted MCP as the standard for their language models, cementing it as a de facto protocol in the AI industry by 2025.
Q4. How does MCP improve data security in AI applications?
MCP improves security by eliminating the need for data buffering. This significantly reduces the risk of breaches and ensures that sensitive information remains within the business or user environment, which is crucial for organizations that handle sensitive data.
Q5. What types of practical applications does MCP have in enterprise environments?
MCP is used in a variety of business applications, including automating internal processes, improving customer service with access to real-time data, generating reports and analytics using natural language, and creating custom virtual assistants with contextualized access to multiple sources of corporate information.
