In recent years, artificial intelligence has advanced rapidly, yet many models still operate in isolation, unable to easily connect with the tools and data around them. In response to this challenge, several initiatives emerged in late 2024 aimed at establishing more efficient ways to integrate AI models with their surrounding environments. Among them, the Model Context Protocol (MCP) stands out—a standard initially developed and promoted by Anthropic, specifically designed to facilitate seamless connections between AI models and external resources. This means enabling models to access up-to-date data, specialized functions, knowledge bases, and external tools in real time, without requiring custom-built integrations for each case.

In other words, MCP functions like a “USB-C” for artificial intelligence: a universal interface that enables smooth, secure, and structured communication between systems, regardless of platform or provider.

Keywords Phrases

• Model Context Protocol (MCP): An open standard developed by Anthropic that enables structured integration between AI models and external resources using JSON-RPC 2.0.
• MCP Client: A component that embeds the protocol into models or cognitive agents, managing connections to MCP servers.
• MCP Server: An entity that exposes functions, data, or resources through a standardized and abstracted interface for the model.
• JSON-RPC 2.0: A lightweight protocol for remote procedure calls using JSON, serving as the foundation for communication in MCP.
• Resources and Prompts: Mechanisms within MCP that allow models to retain contextual information or reusable instructions throughout a session.
• Persistent Sessions: A continuous communication channel between the model and MCP servers, enabling prolonged and contextual interaction.

MCP Technical Architecture: Client, Server and Standardized Communication

Building on this foundation, the Model Context Protocol (MCP) proposes a simple yet powerful architecture to enable such integration: a well-defined client-server model that organizes how AI models access external resources. The MCP Client—typically embedded within the cognitive agent or AI application—is responsible for establishing connections with one or more MCP Servers, which expose functions, data, or instructions in a standardized way.

Each MCP Server translates and abstracts a specific information source, such as a database, a cloud service, a document repository, or even predefined prompts that the model can reuse. This clear separation of responsibilities allows models to interact with multiple services without worrying about the technical details of each one, such as differing response formats, authentication methods, API routes, or custom protocols that would otherwise require manual adaptation.

By operating over a common standard, all that complexity is encapsulated within the MCP Servers, freeing the model to focus solely on the task at hand. Interactions between the client and MCP servers are based on the JSON-RPC 2.0 standard, which ensures that all requests and responses follow a structured and consistent format. This approach—similar to how REST APIs work—allows AI models to invoke functions, query data, or request resources through a unified, clear, and predictable interface.

Figure 1 illustrates this architecture, showing how the MCP Client, integrated within the AI Host (e.g., Claude, ChatGPT), communicates with one or more MCP Servers that act as adapters to external data sources. Each request and response follows a standardized structure, enabling the model to retrieve information or perform actions in a unified and secure manner.

Figure 1: Architecture of the Model Context Protocol (MCP): interaction flow between model, MCP client, server, and data source.

Continuous Context Management: From Isolated Queries to Interactive Sessions

Unlike traditional approaches where AI systems made isolated queries to external systems, MCP enables persistent, bidirectional communication that allows models to maintain active sessions with servers. This means that the model can, for example, request a document, receive it, ask follow-up questions about its content, invoke related functions, and continue interacting with that information throughout an entire workflow. This continuous context handling is made possible through the use of Resources (such as files or relevant data fragments) and Prompts (reusable templates that encapsulate instructions for specific tasks), which the model can retain and reuse across the session. In practice, this allows the model to operate more coherently, maintaining a kind of distributed working memory beyond the immediate prompt—an essential capability for complex tasks like assisted coding, document analysis, step-by-step planning, or multi-agent collaboration.
 

An Interaction Framework for Autonomous and Adaptable Intelligent Agents

Beyond its role as a technical standard, the Model Context Protocol (MCP) provides a structured approach that enables intelligent agents to engage consistently and continuously with their digital environment. By connecting models to external data, tools, and functions through a standardized interface, MCP serves as a channel that organizes how a model can perceive information, maintain relevant context, and act on its environment based on evolving goals.

This interaction model—separating responsibilities between perception (data and resources), memory (persistent sessions), and action (functions and commands exposed by servers)—supports the development of more structured and task-focused agents.

This unified structure delivers several key advantages:

• Rapid and reusable integration of external tools
• Simplified development complexity
• Expanded operational context with timely, accurate information
• Greater autonomy and flexibility for AI agents

Furthermore, its support for persistent sessions enables multi-step, goal-oriented workflows that are essential for enterprise assistants, decision-support systems, and collaborative intelligent environments. This approach allows AI agents not only to reason through tasks, but also to interact effectively with the digital systems around them.

Technical Challenges: Security, Privacy, and Observability in Distributed Environments

While the Model Context Protocol (MCP) represents a major advancement in integrating AI models with external resources, its adoption brings significant technical and operational challenges. One of the primary concerns is security management: in its early versions, MCP lacked a standardized scheme for authentication and authorization, making it difficult to use safely in distributed environments. Although standards like OAuth 2.0 have begun to be incorporated, issues persist regarding consistent access control across servers, especially in multi-user or multi-agent scenarios. Additionally, data privacy is a critical concern, as the model may inadvertently expose parts of the prompt to external servers.

For this reason, many organizations choose to deploy MCP servers in controlled environments and adopt zero-trust principles. Another major challenge is observability: the distributed and dynamic nature of MCP makes it difficult to debug errors or understand why an agent made a specific decision. Currently, there is no unified logging system, which complicates tracing actions across multiple servers. Tools such as inspection wrappers and session tracking systems are being developed, but observability remains a significant limitation in production environments and for advanced debugging workflows.

Use Cases: Actionable Agents in Real-World Contexts

Illustrating real-world use cases is key to understanding the full scope and applicability of the Model Context Protocol (MCP) in actual systems. Beyond its technical definition, these scenarios demonstrate how MCP enables language models to break out of isolation and function as intelligent agents in complex and dynamic environments.

In enterprise settings, for instance, a technical support assistant can combine access to internal documentation, ticketing systems, and administrative tools to deliver personalized solutions, log incidents, or execute commands—all through conversational flows adapted to the user’s situation.

In the financial sector, an agent can integrate real-time market data (data to AI) with a company’s internal positions, using calculation tools to generate up-to-date reports, specific recommendations, and detailed analyses—while always respecting authorization and privacy requirements.

On a personal level, an assistant powered by MCP can function as a secretary, translator, email manager, and file organizer, interacting seamlessly with the user’s various services and devices through a single conversational interface.

In industrial or IoT environments, MCP-enabled agents can read sensor data, make decisions based on specific conditions, and execute automated actions on devices—all expressed through natural language.

Figure 2. MCP Use Cases in Real-World Settings: Technical Support, Financial Analysis, Personal Assistance, and IoT

Figure 2 visually summarizes how the Model Context Protocol enables AI agents to operate effectively across different domains, from corporate tasks to industrial scenarios. In each case, the MCP Client serves as a bridge between the language model and servers that expose data, tools, or actions—allowing agents to execute complex workflows based on a single natural language instruction. Together, these examples highlight the protocol’s potential to reshape how AI agents are designed—not as mere text generators, but as active components within broader, interoperable, and dynamic systems.

Conclusions

The Model Context Protocol represents a key step toward building AI agents that are more connected, autonomous, and adaptable. By standardizing the interaction between models and their environment, MCP not only simplifies technical integration but also enables new cognitive capabilities in real-world contexts. Its adoption promises to transform the way we design, operate, and scale intelligent systems.