The Bridge Between AI and External Tools: The Rise of the MCP Standardization Protocol

The significance brought by artificial intelligence lies in liberating human labor and raising the lower limit of most people's work capabilities. However, current large language models (LLMs) still have limitations, requiring repeated dialogues to provide suggestions, and users must execute these suggestions themselves. This still falls short of the ideal of truly using AI to assist us in our work.

If it is possible to actually use a computer for email replies, report writing, and even automated trading through conversations with AI, this will bring us closer to the vision of liberating productivity. And this technology is the current hot keyword in the AI field - MCP.

What is MCP?

MCP (Model Context Protocol) is a standardized protocol aimed at addressing the issue where past AI models could only "speak" but could not "act." It consists of the following components:

• Model: refers to various large AI language models
• Context: Additional information or external tools provided to the model.
• Protocol: A general, standardized specification or interface

The goal of MCP is to enable AI to not only understand and generate text but also directly manipulate external tools to complete various tasks.

How MCP Works

The operation of MCP involves the following key components:

1. MCP Host: Responsible for managing and coordinating the entire operation of the MCP.
2. MCP Client: Receives user requests and communicates with the AI model.
3. MCP Server: Provides functions that can be used by AI, such as reading databases, sending emails, managing files, etc.

With MCP, AI can not only understand human language but also directly convert specific text into action commands, thereby completing automated operations.

The Importance of MC

1. Bridging AI with external tools: MCP allows AI to access and operate external resources in real-time, overcoming the limitations of traditional LLMs that are confined to pre-trained data.

2. Standardization and Universality: MCP provides a unified specification for different developers, avoiding redundant development and improving integration efficiency.

3. From passive response to active execution: AI can determine what instructions to execute based on real-time conditions and proceed with the next steps according to feedback.

4. Security and Control: MCP controls data access through permission and API key management, ensuring that sensitive information is not leaked.

Comparison between MCP and AI Agent

An AI Agent is an AI system that can automate specific tasks, while MCP is a protocol. MCP can help AI Agents operate more effectively by providing a unified tool interface and standards.

Combining the two allows AI to understand how to act and also know where to act.

Current Related Projects

1. Basic MCP: Allows AI applications to interact with the blockchain, enabling users to deploy contracts or engage in lending through natural language conversations.

2. Lyraos: Multi-AI Agent operating system that allows AI Agents to interact directly with the blockchain to execute cryptocurrency transactions and other operations.

Conclusion

Although MCP provides standardized rules for the interaction between AI and external tools, successful cases in the Web3 field are still limited. This may be due to factors such as the immaturity of technological integration, security and regulatory risks, and user habits and experiences.

The combination of MCP and blockchain has enormous potential, but it also faces dual challenges of technical barriers and market pressure. In the future, if more mature security mechanisms can be integrated, a more intuitive user experience can be created, and truly valuable innovative applications can be explored, "Web3 + MCP" may become the main narrative of the new round.