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Recently, the potential for artificial intelligence (AI) utilization has never been higher. In particular, as large language models (LLMs) have notably advanced, efforts to connect them to various external systems for practical work tools are actively underway.
However, how to effectively link AI with external data remains a technical challenge that needs to be addressed. In this context, the MCP (Model Context Protocol) has emerged as a standard aimed at solving this issue simply yet powerfully.
MCP allows for a seamless connection between AI and external tools, akin to a ‘USB-C’. Through this, MCP is driving significant changes in expanding the utilization scope of AI technology. The introduction of MCP is expected to further enhance the applicability of AI in the future.
MCP and AI Connection Technology Series
What is MCP? Connecting Everything with a Single Standard
MCP is essentially a public protocol that helps AI models integrate various external data sources. Just as USB-C connects multiple devices, by utilizing MCP, AI can seamlessly connect to Google Drive, Slack, databases, and more.
This protocol was proposed by Anthropic and is designed based on JSON-RPC 2.0. This allows AI assistants to perform function calls, data retrieval, command execution, and pre-prompt calls in a consistent manner.
While it may initially seem unfamiliar, the core of this protocol is the creation of an understandable 'common language' between AI and external systems, making interactions with various platforms smoother.
The Core of the Technical Structure: Client-Server Model
MCP operates based on a client-server architecture. The key components of this structure are as follows:
Firstly, the AI host includes various AI applications like Claude or coding assistants. These seek to connect with the external world and can exist in several forms, such as desktop applications, browser extensions, or integrated development environments (IDEs).
Secondly, the MCP client is a module integrated into the AI host that manages connections to various MCP servers. This client is responsible for querying the list of tools or transmitting commands.
Lastly, the MCP server is configured as a lightweight server representing each external system. For example, there are various types such as Slack servers, calendar servers, and file system servers, and these servers provide the functionalities the AI wishes to use in a standardized manner through the MCP interface.
Data sources or tools consist of resources that hold actual data, including local computers, web-based APIs, and cloud storage. The MCP server communicates with these data sources to collect results and transmit them to the client.
This structure enables MCP to facilitate smooth interactions with various external systems, providing users with efficient services.
Reasons Development and Operations Have Become Easier
Previously, AI had to directly deal with APIs and pay attention to authentication processes to utilize new features. However, with the introduction of the MCP structure, this is resolved simply by adding an MCP server.
Because the client is designed to automatically recognize new MCP servers and utilize their functionalities without needing to change the AI model. This approach greatly enhances the scalability and maintenance efficiency of AI.
USB-C for AI: Why is This Analogy Appropriate?
MCP provides a defined port for connecting artificial intelligence with various tools. This is similar to how a USB-C connector connects multiple devices with a single cable. Through MCP, multiple data sources can be connected with a unified standard.
Thanks to this system, artificial intelligence can easily access external information and perform tasks whenever needed. Now, rather than needing to connect separately for each function, an environment has been created where information can be utilized conveniently.
A New Turning Point for AI Integration, MCP
MCP is an element that has the potential to innovatively transform the way AI is utilized, going beyond just technical updates. By integrating complex data integration procedures into a single standard protocol, it has paved the way for AI to be effectively applied in real-world tasks.
In the future, AI is expected to operate smarter by utilizing richer contextual information based on MCP. This is believed to provide significant value to both individual users and businesses.
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