Model Context Protocol (MCP): The key to an AI that "acts" in real time

The Model Context Protocol (MCP) is the technological innovation of 2025 that is transforming Artificial Intelligence models. Simply put, the MCP is a universal language that allows artificial intelligences (such as ChatGPT, Gemini, or Claude) to move beyond being mere "frozen encyclopedias" and become dynamic assistants capable of interacting, taking action, and accessing data in real time.

Presented by Anthropic, the MCP solves the two major limitations of AI systems: their static knowledge and their inability to interact with the outside world.

In this article, we share how the Model Context Protocol (MCP) works, why we need it, and what its benefits are in this new technological era driven by Artificial Intelligence.

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Why do we need the Model Context Protocol (MCP)?

Imagine your AI assistant was trained in 2023. If you ask it for the current price of gold or about your meeting scheduled for tomorrow, it will fail. Why?

1. Frozen Knowledge: Your information stopped at the date of your training.
2. Incapacity to Act: You cannot perform real-world tasks (such as logging into a database or sending an email).

The MCP acts as a secure communication bridge that allows the model to access the live internet, your work software (CRM, calendar, etc.) or any data source to obtain fresh information or execute tasks.

MCP vs. RAG: The Difference Between Talking and Acting

Both RAG (Recovery Augmented Generation) and MCP improve the model information, but in very different ways:

Technology	Main objective	Ability
RAG	Improve the quality of responses (reduce "hallucinations").	Read only: Access internal documents to respond with accurate information.
MCP	Enabling action and automation in the real world.	Read and Act: Access real-time databases, send emails, schedule appointments, etc.

The RAG makes AI speak more intelligently; meanwhile, the Model Context Protocol (MCP) makes AI a functional action agent.

How does the Model Context Protocol (MCP) work? (the simplified process)

The MCP is a standard that allows an AI model to request help from external tools when needed.

Imagine you ask your AI agent: "Find the current price of product X in the inventory and update the shipping status for invoice 456."

1. AI understands: The model knows that for this task it needs two tools: one to "check inventory" and another to "update invoice".
2. MCP Customer Requests Help: The request is structured through a component called MCP Client, which acts as an AI "translator".
3. The MCP Server Acts: The MCP Server is the external software (your inventory/billing system). It receives the request, securely performs the action (queries the database, changes the status), and returns the result.
4. AI responds: With the confirmation of the action and the new information in hand, the model responds: "I have updated the shipping status of invoice 456 and the current price of product X is €15.99."

Thanks to the MCP, the interaction is fluid, safe, and automatic.

Main benefits of the Model Context Protocol

The adoption of the Model Context Protocol (MCP) is becoming widespread in the business world due to its clear advantages:

• Fewer mistakes and "hallucinations": By accessing live and reliable data sources, the LLM stops inventing data and relies on reality, increasing the veracity of their answers.

• Greater automation and utility: AI becomes a working agent that can interact with your company's software (CRM, ERP, databases), automating complex tasks that previously required human intervention.

• Simplified integration: The MCP eliminates the problem of having to create a custom connection for each model and each external tool. It offers an open standard (like USB-C) that makes developing AI applications easier and cheaper.

MCP and Security: A Top Priority

Since the Model Context Protocol (MCP) allows AI to perform actions and access sensitive data, security is critical. To implement it correctly, developers should focus on:

• Explicit consent: The user must give clear permission before the AI ​​accesses data or performs actions.

• Data privacy: Strong access control and encryption are required to ensure that sensitive data is not accidentally shared.

• Verified tools: An external tool should not be trusted unless the MCP Server hosting it is trusted and audited.

The standardization of the Model Context Protocol (MCP)

In addition to everything mentioned above, the Model Context Protocol (MCP) aims to establish a clear standard, something essential in the complex world of software. To understand it, think of a motor connected to various sources of electrical power.

The MCP is like the control panel and wiring of that circuit. It decides what electrical current (information) flows to the engine (the AI ​​model). Input information, including memory, available tools, and past findings, is compared to that "input current."

Like a control panel, the MCP decides which energy sources (data or tools) to connect and when, regulating the flow of information, filtering, and prioritizing inputs. This ensures that only relevant "wires" are activated (i.e., only the appropriate context is loaded) and manages the flow to prevent overloading the system.

Just as a well-designed circuit prevents overloading and ensures efficient energy use, the MCP acts as a connector to facilitate the efficient, relevant, and structured use of context, optimizing the performance of the AI ​​model.

The MCP establishes an open standard and the Plug-and-Play function

The MCP establishes a new open-source standard that the AI ​​engineering community can use to work in a unified way. Standards are not a new concept; for example, REST APIs are an industry standard that provides consistent data exchange between applications.

Similarly, the MCP unifies the LLM and external services by establishing a standard, enabling efficient communication. This standard allows for the use of plug-and-play tools without the need to write custom code to integrate each tool.

It's important to note that the MCP is not an agent framework (like LangChain or LlamaIndex), but rather a standardized integration layer for agents to access tools. It complements these orchestration frameworks, but doesn't replace them; the MCP doesn't decide when a tool is called or for what purpose. It simply provides the standardized connection to optimize tool integration. Ultimately, it's the LLM itself that decides which tools to use based on the context of the user's request.

Detailed Architecture of the Model Context Protocol

The MCP Client/Server model is broken down into three key architectural components:

1. MCP Host: This is the AI ​​application that receives user requests and needs to access the context through the MCP. It can be an integrated development environment (IDE) or a desktop application. It contains the orchestration logic (the brain that directs the request) and can connect each client to a server.
   
2. MCP Client: Communication between the host and the server must pass through this client, which resides within the host. It converts user requests into a structured format that the open protocol can process. Although multiple clients can exist within a single MCP host, each client has a one-to-one relationship with an MCP server. The client acts as a session manager, handling interrupts, errors, reconnections, and analyzing responses to ensure they are relevant and appropriate.
   1. Examples of MCP Clients: IBM® BeeAI, Microsoft Copilot Studio, Claude.ai or Postman.
      
3. MCP Server: This is the external service that provides context to the LLM, translating user requests into server actions. These servers integrate with external tools such as Slack, GitHub, Docker, or a web search engine. They expose real-world functionalities to the model.
   1. Features Exposed by MCP Servers:
      1. Resources: For retrieving information from internal or external databases (they only return data, they do not perform computations).
      2. Tools: For exchanging information with tools that can perform a side effect (such as a calculation, sending an email, or accessing data through an API).
      3. Prompts: Reusable templates and workflows for standard communication between the LLM and the server.

The transport layer: the communication channel

The transport layer is responsible for bidirectional message conversion between clients and servers. It uses JSON-RPC 2.0 messages and employs two main transmission methods:

• Standard Input/Output (stdio): Ideal for integrating local resources (databases or system files) due to its simple and synchronous information transmission (fast for lightweight messages).

• Server Sent Events (SSE): Ideal for integrating remote resources. It uses HTTP POST requests for client-to-server communication and SSE for reverse communication. This format can handle multiple asynchronous and event-driven calls simultaneously.

The MCP is an immensely powerful tool, and its proper implementation must be accompanied by strict cloud governance and continuous auditing processes.

Conclusion

The Model Context Protocol (MCP) represents a constantly evolving approach to LLM tool integration that will continue to mature and transform the industry over time. As technical challenges arise and MCP servers evolve, the standard adapts and MCPs continue to improve.

In any case, the need for standardized tool integration is fundamental for AI agents to operate autonomously and adapt dynamically to real-world environments. With MCP, we can optimize the automation of complex agent workflows to reduce human oversight. In turn, this shift enabled by MCP allows us to dedicate our time to more complex tasks that require human intellect and intuition.

This is the challenge of our present.