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

The field of artificial intelligence has been seeking smarter and more human-like dialogue systems. Traditional chatbots can engage in basic conversations, but they lack personality and depth. To address this issue, developers have introduced the concept of "persona," giving AI specific roles and character traits. However, even with rich personas, AI still only responds passively and cannot proactively execute complex tasks.

To break through this limitation, the Auto-GPT project was born. It allows developers to define tools and functions for AI, enabling the AI to automatically perform tasks according to preset rules. This innovation transforms AI from a passive conversationalist into an active task executor. However, Auto-GPT still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility.

To address these challenges, the Model Context Protocol (MCP) has emerged. MCP aims to simplify the interaction between AI and external tools by providing a unified communication standard. Traditionally, getting large models to perform complex tasks requires extensive coding work, whereas MCP greatly simplifies this process by standardizing interfaces, thus enhancing the efficiency of AI interactions with external tools.

MCP and AI Agent complement each other. The AI Agent focuses on blockchain operations, smart contract execution, and cryptocurrency asset management, while MCP is dedicated to simplifying the interaction between AI and external systems. MCP provides the AI Agent with more efficient cross-platform integration capabilities, significantly enhancing its execution capabilities.

For example, the AI Agent in the DeFi space can obtain real-time market data through MCP and automatically optimize portfolios. MCP also opens up new avenues for collaboration among multiple AI Agents, enabling them to divide tasks and complete complex on-chain data analysis, market forecasting, and risk control management tasks.

In related projects, DeMCP is committed to providing open-source MCP services for AI Agents and offering a revenue-sharing platform for developers. DARK is built on Solana and provides an MCP network in a trusted execution environment. Cookie.fun focuses on AI Agent analysis within the Web3 ecosystem and recently launched a dedicated MCP server. SkyAI is built on the BNB Chain and aims to construct blockchain-native AI infrastructure by expanding MCP.

Despite the significant potential of MCP in enhancing data interaction efficiency, reducing development costs, and increasing security, most MCP projects are still in the proof-of-concept stage. Market trust in these projects is low, mainly due to the long product development cycles and a lack of practical applications. Therefore, accelerating product development, ensuring a close connection between tokens and actual products, and improving user experience have become the core challenges for current MCP projects.

However, the MCP protocol itself still has enormous development potential. With the advancement of AI technology and the maturity of the MCP protocol, it is expected to achieve broader applications in fields such as DeFi and DAO in the future. AI agents can obtain on-chain data in real time through the MCP protocol, execute automated trading, and enhance market analysis efficiency. In addition, 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.

In summary, the MCP protocol, as an important driving force for the integration of AI and blockchain, is expected to become a key engine for the next generation of AI Agents. However, to achieve this vision, challenges in areas such as technological integration, security, and user experience still need to be addressed.