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

1. Introduction to the Concept of MCP

Traditional chatbots in the field of artificial intelligence often lack personalized settings, resulting in monotonous responses. To address this issue, developers have introduced the concept of "character settings," giving AI specific roles and personalities. However, even so, AI remains a passive responder and cannot actively perform complex tasks.

To make AI more proactive, the Auto-GPT project has emerged. It allows developers to define tools and functions for AI, enabling it to automatically perform tasks based on preset rules. Nevertheless, Auto-GPT still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility.

To this end, the 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 greatly reduces the development difficulty and time costs, enabling AI models to interact with external tools more quickly and efficiently.

2. Collaboration between 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, while MCP emphasizes simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability.

MCP provides a unified communication standard for AI Agents to interact with external tools (such as blockchain data, smart contracts, off-chain services, etc.). This standardization addresses the problem of fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect with 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 portfolios through MCP. In addition, MCP opens up new directions for AI Agents, namely collaboration among multiple AI Agents: through MCP, AI Agents can divide functions and work together to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, improving overall efficiency and reliability.

3. Related Projects

1. DeMCP

DeMCP is a decentralized MCP network dedicated to providing self-developed open-source MCP services for AI Agents, offering a deployment platform for MCP developers to share commercial benefits, and achieving one-stop access to mainstream large language models. Developers can access services by supporting stablecoins.

2. DARK

DARK is an MCP network built on a trusted execution environment based on Solana. Its first application is under development, aimed at providing efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access various tools and external services through simple configuration.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing users with comprehensive AI Agent indices and analytical tools. The platform showcases metrics such as the cognitive influence, intelligent following capability, user interaction, and on-chain data of AI Agents, helping users assess the performance of different AI Agents. Recently, the Cookie.API1.0 update introduced a dedicated MCP server, offering plug-and-play MCP services specifically for developers and non-technical users.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to create blockchain-native AI infrastructure by expanding MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, planning to simplify the development process and promote the practical application of AI in blockchain environments through the integration of multi-chain data access, AI agent deployment, and protocol-level utilities.

IV. Future Outlook

The MCP protocol, as a new narrative integrating AI and blockchain, demonstrates tremendous potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, particularly in decentralized finance scenarios 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, resulting in significant fluctuations in their token prices.

In the future, the MCP protocol is expected to achieve broader applications in areas such as DeFi and DAO. AI agents can access on-chain data in real-time through the MCP protocol, executing automated trades and improving market analysis efficiency and accuracy. Furthermore, 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 process of AI assets.

However, to realize this vision, it is still necessary to address challenges in various aspects such as technical integration, security, and user experience. As technology continues to mature and application scenarios expand, the MCP protocol is expected to become an important engine for promoting the development of the next generation of AI Agents, opening up new development directions for artificial intelligence applications.