Artificial intelligence (AI) is moving beyond answering questions to performing real tasks—and that’s changing everything. With increasingly powerful models and the rise of AI agents, a new need has emerged: how can these intelligences be connected to external data sources, tools, and systems in a modular and scalable way?
The answer comes in the form of a new technical standard: the Model Context Protocol (MCP). Designed to be the “universal USB port” (Figure 1) between large language models (LLMs) and the real world, the MCP is quickly becoming the invisible infrastructure behind intelligent agents, automated workflows, and enterprise AI applications.
Figure 1 – MCP a “universal USB port”
AI Generated Image
What is MCP?
MCP was launched by Anthropic in 2024 to address the complexity and scalability challenges of manually connecting AI applications to external APIs. It is an open protocol that standardizes the connection between large language models (LLMs) and external tools such as databases, APIs, file systems, ERPs, CRMs, and other types of systems. It eliminates custom integrations by creating a modular, interoperable channel for AI to access and manipulate real-world information.
It is designed as a “universal layer” for connecting LLMs to any external resource in a secure, auditable, and extensible way.
How does it work? Understanding the architecture
The MCP is based on a client-server architecture and has three main blocks, as illustrated in Figure 2:
- Host: the application where the model lives (such as a chatbot, dashboard, text editor). It incorporates one or more MCP clients.
- MCP client: makes calls to MCP servers. Can handle multiple servers simultaneously.
- MCP server: represents an external tool (like GitHub, Notion, browser, database, etc.) and exposes: Resources, Tools, and Prompts.
Figure 2 – MCP Architecture
Source: Anthropic
Here is a more detailed description of the MCP Server components that are illustrated in Figure 3:
- Resources (data): resources serve as a means of exposing data from the server to the client and can take many forms depending on the type of MCP server you are building. For example, in Playwright’s MCP server that focuses on browser automation, resources might include console logs. In the case of a database MCP server such as SQLite, the resource might be the database file itself. And if you are building a Retrieval-Augmented Generation (RAG) server, the retrieved data might also be considered a resource.
- Tools (executable functions): tools are the core components designed to be controlled by the model. They allow Large Language Models (LLMs) to perform actions on the MCP server. Tools are a powerful primitive within the Model Context Protocol (MCP) that allows servers to expose executable functionality to clients. For example, in a database MCP, tools include operations such as creating or selecting tables. In the context of GitHub MCP, tools might involve actions such as creating a commit or pulling a branch. In browser automation, tools might include navigating to a URL or clicking a button.
- Prompts (instructions): prompts provide a method for exposing prompt templates from the server to the client, standardizing common interaction patterns. For example, on a GitHub server, prompts can be exposed to create a standard commit message from code. This simplifies the process because instead of writing prompts on the client side, server developers can create prompt templates for commonly needed interactions and expose them through a prompt artifact.
Figure 3 – MCP protocol operation
Source: Anthrop\c
Communication can occur via local stdio process (standard input output), or network streaming HTTP + SSE or streamable HTTP.
Real and advanced use cases
- Blender + Claude Desktop: using Docker, it is possible to create MCP servers for tools like Blender. This allows, for example, an LLM to create or modify 3D scenes with natural language commands.
- Perplexity Windows: On Windows, MCP-enabled agents can access local files, terminal, web browsing, and applications, with controlled logs and permissions.
- Corporate Integrations: companies like Microsoft, AWS, IBM and startups like Triggo.ai already use MCP to integrate AI into proprietary databases with compliance.
A powerful protocol, but not without risks
The more power we give to a model, the more security it needs. And with MCP, two risks come to the fore:
- Prompt Injection: technique in which an attacker injects hidden commands into the text sent to the model. The agent can be manipulated to perform incorrect tasks, leak data, or disobey rules.
- Tool Poisoning: manipulation of tools connected to the model. If an MCP server is compromised, it can provide erroneous responses, execute dangerous commands, or act as a Trojan horse.
Best practices for safe use of MCP
- Input validation: never blindly trust what comes from the model or the user.
- Audit and logs: log all calls and responses between MCP clients and servers.
- Permissions control: do not expose more tools than necessary.
- Cross-checking answers: use redundancy to avoid manipulated responses.
- Isolation: limit the scope of commands executed by tools.
Conclusion
MCP is more than a protocol—it’s a strategic step toward making AI truly integrated, contextual, and autonomous. It transforms models into agents capable of acting in the real world with precision and responsibility. But with great power comes great responsibility: it’s essential that MCP-based architectures are built with security, governance, and transparency in mind from the very first command.
If you’re building the future of AI, MCP will be your path. The question is: will you be ready?
If you enjoy this content, maybe you would like to read more about RAG: The Secret Behind AIs That Truly Understand Your Business.
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