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

1. Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often lack personalized character settings, leading to uniform responses that lack human touch. To address this issue, developers have introduced the concept of "character setting," endowing AI with specific roles, personalities, and tones. However, even with rich "character settings," AI remains a passive responder, unable to proactively execute tasks or perform complex operations.

To enable AI to autonomously execute tasks, the open-source project Auto-GPT has emerged. It allows developers to define a set of tools and functions for the AI and register these tools within the system. When a user makes a request, Auto-GPT generates operational instructions based on preset rules and tools, automatically executing tasks and returning results. This transforms AI from a passive conversationalist into an active task executor.

Despite the fact that Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as non-uniform tool invocation formats and poor cross-platform compatibility. To address these challenges, 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, to enable large-scale models to perform complex tasks, developers need to write a large amount of code and tool documentation, which significantly increases development difficulty and time costs. The MCP protocol simplifies this process significantly by defining standardized interfaces and communication specifications, allowing AI models to interact with external tools more quickly and effectively.

2. The Integration of MCP and AI Agent

The relationship between MCP and AI Agent is complementary. The AI Agent mainly focuses on blockchain automation operations, smart contract execution, and crypto asset management, emphasizing privacy protection and decentralized application integration. MCP, on the other hand, focuses on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility. The AI Agent can achieve more efficient cross-platform integration and operations through the MCP protocol, improving execution capabilities.

Traditional AI Agents, while possessing certain execution capabilities, such as executing transactions and managing wallets through smart contracts, often have predefined functions that lack flexibility and adaptability. The core value of MCP lies in providing a unified communication standard for the interaction between AI Agents and external tools (including blockchain data, smart contracts, off-chain services, etc.). This standardization addresses the issue of interface fragmentation 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 obtain market data in real-time and automatically optimize their portfolios through MCP. In addition, MCP opens up a new direction for AI Agents, namely collaboration among multiple AI Agents: through MCP, AI Agents can collaborate by function, combining 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 trading automation, MCP connects various trading and risk control Agents, addressing issues like slippage, transaction friction, and MEV during trading, achieving safer and more efficient on-chain asset management.

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 with shared commercial revenue, and achieving one-stop access to mainstream large language models (LLM). Developers can obtain services through stablecoin support. As of May 8, its token DMCP has a market value of approximately 1.62 million dollars.

2. DARK

DARK is a trust execution environment ( TEE ) based on Solana, supporting the MCP network. Its token $DARK has been launched on a trading platform, with a market value of approximately $11.81 million as of May 8. Currently, the first application of DARK is under development, which will provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access various tools and external services with simple configurations. Although the product has not been fully released, users can join the early experience phase via an email waiting list to participate in testing and provide feedback.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, aimed at providing users with comprehensive AI Agent indices and analytical tools. The platform helps users understand and evaluate the performance of different AI Agents by showcasing indicators such as the mental influence, intelligent following ability, user interaction, and on-chain data of AI Agents. On April 24th, the Cookie.API 1.0 update launched a dedicated MC server, which includes plug-and-play AI Agent specific MC servers designed for developers and non-technical users, requiring no configuration.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to construct blockchain-native AI infrastructure by expanding MCP. The platform provides scalable and interoperable data protocols for Web3-based AI applications, planning to simplify the development process through the integration of multi-chain data access, AI agent deployment, and protocol-level utilities, thus promoting the practical application of AI in the blockchain environment. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with over 10 billion rows of data, and will soon launch MCP data servers supporting the Ethereum mainnet and Base chain. Its token SkyAI is listed on a trading platform, with a market cap of approximately $42.7 million as of May 8.

4. Future Development

The MCP protocol, as a new narrative of the fusion of AI and blockchain, has shown great potential in improving data interaction efficiency, reducing development costs, enhancing security, and protecting privacy, especially in decentralized finance scenarios where it has a wide application prospect. However, most current projects based on MCP are still in the proof-of-concept stage and have not launched mature products, leading to a continuous decline in their token prices after going live. This reflects a trust crisis in the market regarding MCP projects, mainly stemming from a long product development cycle and a lack of practical application deployment.

How to accelerate product development progress, ensure a close connection between tokens and actual products, and enhance user experience will be the core issues faced by the current MCP project. In addition, the promotion of the MCP protocol in the crypto ecosystem still faces challenges in technical integration. Due to differences in smart contract logic and data structures between different blockchains and DApps, a unified standardized MCP server will still require significant development resources.

Despite facing the challenges mentioned above, the MCP protocol itself still demonstrates significant market development potential. With the continuous advancement of AI technology and the gradual maturity 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. 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 of AI assets.

The MCP protocol, as an important auxiliary force for the integration of AI and blockchain, is expected to become a significant engine for driving the next generation of AI Agents as technology matures and application scenarios expand. However, achieving this vision still requires addressing challenges in various aspects, including technical integration, security, and user experience.