AI and MC: A New Era of Liberating Productivity

The emergence of artificial intelligence has brought hope for the liberation of labor and the improvement of work efficiency for humanity. However, current large language models (LLM) still have limitations, requiring repeated dialogue to provide suggestions, and users must personally implement these suggestions. This still falls short of the true vision of using AI to assist us in our work.

If it is possible to utilize computers for tasks such as replying to emails, writing reports, and even automated trading through conversations with AI, it will be closer to the goal of liberating productivity. This technology is currently a hot topic in the field of AI - MC.

Definition and Operation of MC

MCP(Model Context Protocol) is a standardized protocol designed to address the problem that AI models can only "speak" but cannot "act". It consists of three key components:

1. Model: Refers to various large language models in AI.
2. Context: Additional information or external tools provided to the model.
3. Protocol: A universal, standardized specification or interface

The core of MCP lies in establishing unified standards, enabling AI not only to converse but also to directly manipulate external tools to complete various tasks.

The operation of MCP involves three main components:

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

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

The Importance of MC

1. Bridging AI with external tools: MCP enables AI to truly "take action" rather than just providing static information.

2. Standardization and Universality: MCP provides a unified standard for the interaction between AI and external tools, avoiding redundant development.

3. From passive response to active execution: AI can decide to execute instructions based on actual conditions and adjust subsequent actions according to feedback.

4. Security and Control: MCP allows for data access control through permission and API key management, ensuring the safety of sensitive information.

Comparison between MCP and AI Agent

The AI Agent emphasizes the ability of AI to take proactive actions and execute tools, while MCP focuses on how to enable different AI models to communicate with external tools, playing the role of a universal standard. MCP provides a more efficient operating environment for the AI Agent, making it easier to interact with various tools and platforms.

Current MCP Concept Projects in the Cryptocurrency Field

1. The framework officially developed by Base: allows AI applications to interact with the Base blockchain, enabling users to deploy contracts and use financial services through natural language conversations.

2. Flock: A decentralized AI training platform that offers Web3 agent 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 perform cryptocurrency transactions and other operations.

Conclusion

Although MCP provides standardized rules for the interaction of 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, user experience issues, and market fatigue towards AI projects.

The combination of MCP and blockchain has immense potential, but it also faces dual challenges of technical barriers and market pressure. In the future, if issues such as security mechanisms and user experience can be resolved, and truly valuable innovative applications can be developed, "Web3 + MCP" may break through the limitations of hype and become the mainstream narrative of a new round.