AI and MC: A New Opportunity to Liberate Productivity

The emergence of artificial intelligence has brought unprecedented opportunities for humanity, with its core significance lying in the liberation of labor and the enhancement of work efficiency. However, the current large language model (LLM) still has many limitations, requiring repeated dialogues to provide suggestions, and users still need to personally execute these suggestions. There is still a certain gap between this and the vision of truly leveraging AI to assist in work.

Nowadays, through conversations with AI, we can achieve functions such as automatic email replies, report writing, and even automated trading. These advancements bring us one step closer to the goal of liberating productivity. The key technology that enables this breakthrough is the currently highly regarded MCP in the field of AI.

MCP: Bridging AI and the External World

MCP (Model Context Protocol) is a standardized protocol released by Anthropic in November 2024, aimed at addressing the problem where AI models can only "speak" but cannot "act".

The core components of MCP include:

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

In short, MCP enables AI to not only engage in conversation but also directly manipulate external tools to accomplish various tasks through standardized protocols.

How MCP Works

The operation of MCP involves three main roles:

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 a set of annotated APIs that support various functions for AI.

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

The Importance of MCP

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

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

3. From passive response to proactive execution: MCP enables AI to make decisions and execute instructions based on real-time conditions, greatly enhancing the practicality of AI.

4. Security and Control: MCP ensures the security of sensitive information through mechanisms such as permission management and API keys.

Comparison between MCP and AI Agent

AI Agent emphasizes the proactive capabilities of AI, while MCP focuses on how to standardize communication between different AI models and external tools. MCP provides a more efficient and secure operating environment for AI Agent.

MCP Applications in the Cryptocurrency Field

1. Base MCP: Allows AI applications to interact with the Base blockchain, enabling smart contract deployment and DeFi operations.

2. Flock: Provides a decentralized AI training platform, dedicated to running AI-driven blockchain tasks locally.

3. LYRAOS: Allows AI Agents to interact directly with the Solana blockchain to perform operations such as cryptocurrency transactions.

Conclusion

Although MCP has shown great potential in the Web3 field, its successful cases remain limited. This may stem from various factors such as the immaturity of technology integration, security risks, and user experience. In addition, the market's attitude towards AI projects is also becoming cautious.

In the future, the integration of MCP and blockchain still needs to overcome technical barriers and market pressures. Only by improving security mechanisms, optimizing user experience, and developing truly valuable innovative applications can the concept of "Web3+MCP" go beyond speculation and become an important force driving industry development.