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

1. MCP Concept Analysis

Traditional chatbots often lack personalized settings, resulting in monotonous responses. To address this issue, developers introduced the concept of "character persona," giving AI specific roles and tones. However, even with a rich "character persona," AI remains a passive responder and cannot proactively execute complex tasks.

To break through this limitation, the Auto-GPT project has emerged. It allows developers to define tools and functions for the AI and register them in the system. After a user makes a request, Auto-GPT can automatically generate operation instructions and execute tasks according to preset rules and tools. This transforms the AI from a passive conversationalist to an active task executor.

Although Auto-GPT has achieved autonomous execution of AI, it still faces issues such as inconsistent tool invocation 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 invoke various external services. Traditionally, getting large models to perform complex tasks requires a lot of code and instructions, while MCP significantly simplifies this process through standardized interfaces, improving the efficiency of AI's interaction with external tools.

2. Collaboration between MCP and AI Agent

MCP and AI Agent complement each other. AI Agent mainly 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 AI Agent and external systems, providing standardized protocols and context management to enhance cross-platform interoperability.

The core value of MCP lies in providing a unified communication standard for 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 integrate multi-chain data and tools, significantly enhancing their autonomous execution capabilities. For example, DeFi-type AI Agents can access market data in real-time and automatically optimize their portfolios through MCP.

In addition, MCP has opened up new directions for AI Agents: multi-Agent collaboration. Through MCP, different AI Agents can divide their functions and work together to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby enhancing overall efficiency and reliability. In terms of on-chain transaction automation, MCP connects various trading and risk control Agents, helping to address issues like slippage, transaction wear, and MEV, achieving safer and more efficient on-chain asset management.

3. Overview of Related Projects

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 revenue, and enables one-stop access to mainstream large language models. Developers can obtain services through stablecoins.

2. DARK: The MCP network built on a TEE environment based on Solana. Its first application is under development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols.

3. Cookie.fun: A platform focused on AI Agents within the Web3 ecosystem, providing AI Agent indices and analytical tools. The platform showcases metrics such as the cognitive influence, intelligent following ability, user interaction, and on-chain data of AI Agents, helping to assess the performance of different AI Agents.

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

IV. Future Prospects

The MCP protocol, as a new narrative that integrates AI and blockchain, demonstrates tremendous potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, particularly in decentralized finance and other fields with broad application prospects. However, most current projects based on MCP are still in the proof-of-concept stage and have not launched mature products, resulting in a continuous decline in their token prices, reflecting a crisis of trust in the MCP projects in the market.

In the future, the MCP protocol is expected to achieve broader applications in areas such as DeFi and DAO. AI agents can use the MCP protocol to access 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 may also provide a transparent and traceable operating platform for AI models, promoting the decentralization and assetization of AI assets.

Despite facing challenges in technical integration, security, user experience, and other areas, the MCP protocol, as an important auxiliary force for the integration of AI and blockchain, is expected to become a key engine driving the development of the next generation of AI Agents. As technology matures and application scenarios expand, the potential of the MCP protocol in the field of artificial intelligence applications will be further unlocked.