MCP and AI Agent: A New Paradigm for Artificial Intelligence Applications

Introduction to MCP Concept

In the field of artificial intelligence, traditional chatbots often rely on generic dialogue models, lacking personalized settings, which leads to monotonous and dull responses. To address this issue, developers have introduced the concept of "personality", giving AI specific roles, characteristics, and tones to make its responses more aligned with user expectations. However, even with a rich "personality", AI remains a passive responder, unable to proactively execute tasks or perform complex operations.

To break through this limitation, the open-source project Auto-GPT has emerged. It allows developers to define tools and functions for AI and register them in the system. When users make requests, Auto-GPT generates operation instructions based on preset rules and tools, automatically executing tasks and returning results, transforming the AI from a passive responder to an active task executor.

Despite the fact that Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility. To address this, MCP (Model Context Protocol) has emerged. MCP aims to simplify the interaction between AI and external tools by providing a unified communication standard, allowing AI to easily call various external services. This protocol significantly reduces development difficulty and time costs, enabling AI models to interact with external tools more efficiently.

The Synergistic Effect of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on blockchain automation operations, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and decentralized application integration. MCP, on the other hand, focuses on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility.

The core value of MCP lies in providing a unified communication standard for the interaction between AI Agents and external tools (such as blockchain data, smart contracts, off-chain services, etc.). This standardization addresses the issue of fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing autonomous execution capabilities. For example, DeFi-type AI Agents can obtain market data in real-time and automatically optimize their investment portfolios through MCP.

In addition, MCP opens up a new direction for AI Agents, namely multi-Agent collaboration. Through MCP, different AI Agents can collaborate according to functional divisions to jointly complete complex tasks such as on-chain data analysis, market prediction, and risk control management, thereby improving overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents to address issues such as slippage, trading friction, and MEV in transactions, achieving safer and more efficient on-chain asset management.

Related Project Overview

1. DeMCP: A decentralized MCP network that provides self-developed open-source MCP services for AI Agents, offers a deployment platform for developers to share commercial profits, and achieves one-stop access to mainstream large language models.

2. DARK: The MCP network under the trusted execution environment (TEE) built on Solana. Its first application is currently in development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols.

3. Cookie.fun: A platform focused on AIAgent in the Web3 ecosystem, offering comprehensive AI Agent indices and analytical tools. This platform showcases metrics such as the mental influence of AI Agents, smart following capabilities, user interactions, and on-chain data.

4. SkyAI: A Web3 data infrastructure project built on the BNB Chain, which constructs a blockchain-native AI infrastructure by extending the MCP. This platform provides a scalable and interoperable data protocol for Web3-based AI applications, supporting multi-chain data access and AI agent deployment.

Future Outlook and Challenges

The MCP protocol, as a new paradigm for the integration of AI and blockchain, demonstrates great potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, particularly in areas such as decentralized finance, where it has broad application prospects. However, most projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products, leading to significant fluctuations in their token prices.

The main challenges include:

• Accelerate product development progress
• Ensure that the tokens are closely linked to the actual product.
• Improve user experience
• Overcome technical integration barriers, unify the smart contract logic and data structures between different blockchains and DApps.

Despite facing challenges, the MCP protocol still demonstrates enormous market potential. With advancements in AI technology and the maturation of the MCP protocol, it is expected to achieve broader applications in fields such as DeFi and DAO in the future. For example, AI agents can use the MCP protocol to obtain on-chain data in real-time, execute automated trades, and enhance the efficiency and accuracy of market analysis.

The decentralized nature of the MCP protocol is expected to provide a transparent and traceable operating platform for AI models, promoting the decentralization and assetization of AI assets. As an important auxiliary force in the integration of AI and blockchain, the MCP protocol is poised to become a key engine driving the next generation of AI Agents. However, achieving this vision still requires addressing various challenges such as technical integration, security, and user experience.