The Integration of AI and MCP Technology: A New Trend in Liberating Productive Forces

The emergence of artificial intelligence aims to free human labor and improve work efficiency. However, current large language models still have limitations, requiring repeated dialogue to provide suggestions, and users must execute them personally. This is still a considerable distance from truly leveraging AI to assist us in our work.

If it is possible to utilize computers for email replies, report writing, and even automated trading through conversations with AI, it will bring us closer to the vision of liberating productivity. This technology is currently a hot topic in the AI field - MCP.

Definition and Operation of MCP

MCP (Model Context Protocol) is a standardized protocol designed to address the issue where AI models can only "speak" but cannot "act." It consists of three components: Model, Context, and Protocol, which enables AI to not only converse but also directly manipulate external tools to accomplish various tasks.

The operation of MCP involves three main components:

1. MCP Host (Administrator): Responsible for managing and coordinating the operation of the entire MCP.
2. MCP Client: Receives user requirements and communicates with the AI model.
3. MCP Server: A collection of functional APIs available for AI use.

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

The Importance of MC

1. Connecting AI with external tools: MCP enables AI to access real-time data and perform practical operations, overcoming the limitations of traditional language models.

2. Standardization and Universality: MCP provides a unified development standard for different manufacturers, avoiding redundant development and improving efficiency.

3. From passive response to active execution: AI can decide which instructions to execute based on the situation and adjust its actions according to feedback.

4. Security and Control: MCP controls data access through permissions and API key management, ensuring the security of sensitive information.

Comparison between MCP and AI Agent

An AI Agent is an AI system that can automatically handle specific tasks, while MCP is a protocol. MCP provides standardized tool interfaces for AI Agents, enabling them to operate more effectively. AI Agents focus on decision-making and logic, while MCP addresses issues related to tool interfaces and standard formats. The combination of the two allows the AI to know how to act and where to act.

MCP Applications in the Blockchain Field

1. Base MCP: Allows AI applications to interact with the Base blockchain, enabling users to deploy contracts and use DeFi features through natural language conversations.

2. Flock: A decentralized AI training platform that provides Web3 proxy models, enabling AI-driven blockchain tasks to run locally.

3. LYRAOS: A multi-AI Agent operating system that allows AI Agents to interact directly with the Solana 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 immature technology integration, security risks, user experience issues, and market fatigue towards AI projects.

The combination of MCP and blockchain has potential, but faces dual challenges in technology and market. If security issues can be resolved, user experience improved, and truly valuable innovative applications developed in the future, "Web3 + MCP" may become a major narrative in the next wave. However, the market remains cautious about this for now and needs time to observe its development.