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MCP: The Key Technology from "Speaking" to "Doing" for AI, Liberating a New Era of Productivity
AI and MC: A New Chapter in Liberating Productivity
The emergence of artificial intelligence has brought the possibility of liberating human labor and has raised the basic level of most jobs. However, current large language models (LLM) still have limitations, requiring multiple rounds of dialogue to provide suggestions, and users still need to implement these suggestions themselves. This still falls short of the ideal of truly leveraging AI to assist us in our work.
If we can utilize computers for tasks such as replying to emails and writing reports through conversations with AI, and even automate trading, 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 Function of MCP
MCP (Model Context Protocol) is a standardized protocol designed to address the issue where past AI models could only "speak" but could not "act." It consists of the following three parts:
The core of MCP is to enable AI not only to understand and generate text but also to directly manipulate external tools to complete various tasks. This stands in stark contrast to traditional LLMs (such as ChatGPT, Grok, etc.), which are limited to text input and output.
The Operating Mechanism of MC
The operation of MCP involves three main components:
Through MCP, AI can directly convert specific text into action commands, achieving automation. This allows AI to perform tasks such as organizing sales reports, sending customer emails, and even operating in 3D modeling software.
The Importance of MCP
Bridging AI with external tools: MCP enables AI to access and manipulate the latest data in real-time, addressing the limitations of traditional LLMs that are constrained by pre-training data.
Standardization and Universality: MCP provides a unified specification for different developers, reducing the problem of redundant development and improving efficiency.
From passive response to active execution: AI can decide which instructions to execute based on real-time conditions and take further actions based on feedback, greatly enhancing its practicality.
Security and Control: MCP controls data access through methods such as permissions and API key management, ensuring the security of sensitive information.
Comparison between MCP and AI Agent
MCP is a protocol, while AI Agent is a concept or execution method. MCP focuses on standardizing communication between AI and external tools, while AI Agent emphasizes the autonomous action capability of AI. MCP provides AI Agent with a more efficient and secure way to access external resources, and the combination of the two allows AI to know both how to act and where to act.
MCP Applications in the Blockchain Field
Base MCP: Allows AI applications to interact with the Base blockchain, enabling users to deploy contracts or use DeFi services through natural language conversations.
Flock: Provides a decentralized AI training platform aimed at allowing AI-driven blockchain tasks to run locally, increasing user control.
LYRAOS: Allows AI Agents to interact directly with the Solana blockchain to perform cryptocurrency transactions and other operations.
Summary
Although MCP provides a standardized solution 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, user experience issues, and market fatigue towards AI projects.
The combination of MCP and blockchain holds great potential, but it also faces dual challenges from technology and the market. In the future, if security mechanisms can be improved, user experience optimized, and truly valuable innovative applications developed, "Web3 + MCP" may become the next mainstream narrative, rather than just a hyped topic.