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

The field of artificial intelligence has been seeking more personalized and proactive interaction methods. Traditional chatbots often lack personality, and to address this issue, developers have introduced the concept of "character setting," giving AI specific roles and tones. However, even so, AI remains a passive responder and cannot proactively execute complex tasks.

To address this limitation, the Auto-GPT project has emerged. It allows developers to define tools and functions for AI, enabling AI to automatically perform tasks based on user requests. Although Auto-GPT has made significant progress in achieving AI autonomy, it still faces challenges such as inconsistent tool invocation formats and poor cross-platform compatibility.

In response to 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, enabling AI to easily call various external services. This significantly reduces development difficulty and time costs, allowing AI models to interact with external tools more quickly and effectively.

MCP and AI Agent complement each other. The AI Agent focuses on automated operations on the blockchain, execution of smart contracts, and management of crypto assets, while MCP emphasizes simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management. The core value of MCP lies in providing a unified communication standard for the interaction between the AI Agent and external tools (such as blockchain data, smart contracts, off-chain services, etc.). This standardization solves the problem of fragmented interfaces in traditional development, enabling the AI Agent to seamlessly interface with multi-chain data and tools, significantly enhancing its autonomous execution capabilities.

In the DeFi field, AI Agents can obtain market data in real-time through MCP and automatically optimize their portfolios. Moreover, MCP opens up new directions for AI Agents, allowing for collaboration among multiple AI Agents. Through MCP, different AI Agents can work according to their functions to collaboratively complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby enhancing overall efficiency and reliability.

Currently, multiple projects are exploring the integration of MCP with AI Agents:

1. DeMCP: A decentralized MCP network that provides self-developed open-source MCP services for AI Agents and offers a deployment platform for MCP developers with shared commercial revenue.

2. DARK: The MCP network built on Solana operates under a Trusted Execution Environment (TEE). Its first application is currently in development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol.

3. Cookie.fun: A platform focused on AI Agents in the Web3 ecosystem, providing AI Agent indexes and analytical tools. The recent update has launched a dedicated MCP server, offering plug-and-play MCP services for developers and non-technical users.

4. SkyAI: A Web3 data infrastructure project built on the BNB Chain, designed to construct a blockchain-native AI infrastructure by expanding MCP. Currently supports aggregated datasets from BNB Chain and Solana, and will also support MCP data servers from the Ethereum mainnet and Base chain in the future.

Although the MCP protocol has shown great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, most current projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products. This has led to a continuous decline in the token prices after their launch, reflecting a crisis of trust in the MCP projects in the market.

In the future, the development of the MCP protocol faces challenges such as accelerating product development speed, ensuring a close connection between tokens and actual products, and enhancing user experience. At the same time, the promotion of the MCP protocol in the cryptocurrency ecosystem still needs to overcome technical integration barriers, such as unifying the differences in smart contract logic and data structures between different blockchains and DApps.

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

As an important auxiliary force in the integration of AI and blockchain, the MCP protocol is expected to become a key engine for driving the next generation of AI Agents. However, achieving this vision still requires addressing various challenges, including technical integration, security, and user experience. As these issues are gradually resolved, the combination of MCP and AI Agents will open up new development space for artificial intelligence applications.