The era of the Internet of Intelligent Agents has arrived. The MCP protocol, which is equivalent to the http protocol, allows intelligent agents to seamlessly connect with data sources.

The era of the Internet of Intelligent Agents has arrived. The MCP protocol is equivalent to the HTTP protocol of the Internet era. It opens up data communication between various terminals such as large models, intelligent agents, RAGs, tools, etc., thus realizing the interconnection of AI applications. The communication problem between intelligent agents has been solved, which means the arrival of the era of the Internet of Intelligent Agents.

What is MCP?

MCP (Model Context Protocol) is an open source protocol that aims to provide a standardized interface for AI models to interact with external data sources (such as Google Drive, Dropbox, etc.). Simply put, it is like giving AI a "translator" so that AI can access different data sources through a unified "language" and smooth out the differences between services.

Why do we need MCP?

In traditional AI development, connecting different data sources requires writing separate code for each service (such as Google Drive, Dropbox). This approach is not only cumbersome, but also increases development and maintenance costs. MCP solves this problem by providing a standardized interface. Developers only need to write code once based on MCP to support multiple data sources, making the interaction between AI models and external data more efficient.

The role of MCP Server

MCP Server is the "translator" of the entire system, responsible for converting AI requests into API calls of specific data sources. Its core responsibilities include:

• Request mapping: For example, translating MCP's standard request list_files into Google Drive's files.list.
• Mechanical execution: It is not responsible for understanding the meaning of the request and is only focused on converting requests and responses between the MCP and the data source.
• Authentication management: handles authentication with data sources (such as OAuth) to ensure access security.

For example, when AI requests "list PDF files", MCP Server converts it into an API call to Google Drive, obtains the results, and then translates them back to MCP format.

The "intelligent" role of MCP Client

The MCP Client (e.g. a virtual assistant) is the “spokesperson” of the AI, responsible for interacting with the user and generating MCP requests. Its tasks include:

• Understanding user intent: Parsing natural language requests such as "list PDF files from the last month".
• Generate standard requests: Convert user requirements into MCP API calls.
• Processing returned data: Converting the response from the MCP Server into a user-friendly answer.

The MCP Client requires a certain amount of intelligence to interpret user needs, while the MCP Server focuses on the underlying execution work.

Overall workflow

• The user makes a request (eg, "list the PDF files from the last month").
• MCP Client parses the request and generates MCP standard calls (such as list_files).
• MCP Server receives the request and translates it into API calls for specific services (such as files.list for Google Drive).
• The data source returns the result to the MCP Server.
• The MCP Server converts the result back to the MCP format and sends it back to the MCP Client.
• The MCP Client processes the data and generates user-friendly responses.

This division of labor allows AI to focus on intelligent tasks, while the complexity of data access is handled by the MCP Server.

in conclusion

MCP uses standardized interfaces to allow AI models to easily connect to a variety of data sources, greatly simplifying the development process. MCP Server is responsible for "translation" and execution, while MCP Client focuses on intelligent interaction. This modular design not only improves efficiency, but also lays the foundation for future expansion. If you are interested in the collaboration between AI and data sources, MCP is worth a try!