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

1. Introduction to MCP Concept

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

To overcome this limitation, the Auto-GPT project was born. It allows developers to define tools and functions for AI, enabling the AI to automatically execute tasks based on predefined rules. Nevertheless, Auto-GPT still has shortcomings in terms of uniformity in tool invocation formats and 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. This significantly reduces development difficulty and time costs, allowing AI models to interact with external tools more efficiently.

2. Collaboration between MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent focuses on blockchain 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.

MCP provides a unified communication standard for AI Agents and external tools (such as blockchain data, smart contracts, etc.), addressing the issue of fragmented interfaces. This allows AI Agents to seamlessly connect to multi-chain data and tools, significantly enhancing their autonomous execution capabilities. For example, DeFi-type AI Agents can use MCP to obtain market data in real-time and optimize their investment portfolios.

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

III. Introduction to Related Projects

1. DeMCP

DeMCP is a decentralized MCP network that provides self-researched open-source MCP services for AI Agents, offering a revenue-sharing platform for developers to achieve one-stop access to mainstream large language models. Developers can obtain services through stablecoins.

2. DARK

DARK is an MCP network built on a trusted execution environment (TEE) based on Solana. Its first application is currently under development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol.

3. Cookie.fun

Cookie.fun focuses on AI Agents in the Web3 ecosystem, providing comprehensive Agent indices and analytical tools. The platform showcases metrics such as the mental influence and intelligent following ability of Agents, helping users assess the performance of different Agents. Recent updates have introduced dedicated MCP servers, offering developers plug-and-play solutions.

4. SkyAI

SkyAI is a Web3 data infrastructure project based on the BNB Chain, building a blockchain-native AI infrastructure through the extension of MCP. The platform provides a scalable and interoperable data protocol for Web3 AI applications, simplifying the development process and promoting the practical application of AI in a blockchain environment.

4. Future Development Prospects

The MCP protocol shows great potential in improving data interaction efficiency, reducing development costs, and enhancing security, particularly in the decentralized finance sector, where it has broad application prospects. However, most MCP projects are still in the proof-of-concept stage and face challenges such as long product development cycles and a lack of practical applications.

Nevertheless, the MCP protocol still shows great potential for market development. With advances in AI technology and the maturity of the protocol, MCP is expected to achieve broader applications in areas such as DeFi and DAO. For instance, AI agents can use MCP to access on-chain data in real-time, execute automated trades, and enhance market analysis efficiency.

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. 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 multiple challenges, including technical integration, security, and user experience.