1. Introduction to MC concept

In the field of artificial intelligence, traditional chatbots have relied heavily on generic conversation models, lacking personalized character settings, resulting in responses that often appear monotonous and lack human touch. To address this issue, developers have introduced the concept of ‘character setting,’ giving AI specific roles, personalities, and tones to make its responses more tailored to user expectations. However, even with rich ‘character settings,’ AI remains merely a passive responder, unable to actively perform tasks or carry out complex operations. Hence, the open-source project Auto-GPT was born. Auto-GPT allows developers to define a series of tools and functions for AI and register these tools in the system. When users make requests, Auto-GPT generates corresponding operation instructions based on preset rules and tools, automatically performs tasks, and returns results. This approach transforms AI from a passive conversationalist to an active task-oriented AI.

Although Auto-GPT to some extent achieves the autonomous execution of AI, it still faces problems such as inconsistent tool invocation formats and poor cross-platform compatibility. To address these issues, MCP (Model Context Protocol) has emerged, aiming to tackle the main challenges that AI faces in the development process, especially the complexity of integrating with external tools. The core goal of MCP is to simplify the interaction between AI and external tools by providing a unified communication standard, enabling AI to easily invoke various external services. Traditionally, to enable large-scale models to perform complex tasks (such as querying weather or accessing web pages), developers need to write a large amount of code and tool instructions, greatly increasing the difficulty and time cost of development. However, the MCP protocol significantly simplifies this process by defining standardized interfaces and communication specifications, allowing AI models to interact with external tools more quickly and effectively.

Two, Fusion of MCP and AI Agent

MCP and encrypted AI Agent complement each other, with the main difference being that AI Agent focuses on the automation of blockchain operations, smart contract execution, and encrypted asset management, emphasizing privacy protection and the integration of decentralized applications. MCP focuses more on simplifying the interaction between AI Agent and external systems, providing standardized protocols and context management to enhance cross-platform interoperability and flexibility. Encrypted AI Agent can achieve more efficient cross-platform integration and operation through the MCP protocol, thereby enhancing its execution capabilities.

Previously, AI Agents had certain execution capabilities, such as executing trades and managing wallets through smart contracts. However, these functions are usually predefined, lacking flexibility and adaptability. The core value of MCP lies in providing a unified communication standard for AI Agents to interact with external tools, including blockchain data, smart contracts, off-chain services, etc. This standardization addresses the problem of interface fragmentation in traditional development, enabling AI Agents to seamlessly integrate with multi-chain data and tools, significantly enhancing their autonomous execution capabilities. For example, a DeFi AI Agent can use MCP to real-time access market data and automatically optimize investment portfolios. Additionally, MCP opens up new directions for AI Agents, such as collaboration among multiple AI Agents: through MCP, AI Agents can collaborate based on functional division of labor, combining to complete complex tasks such as on-chain data analysis, market forecasting, risk management, etc., improving overall efficiency and reliability. On-chain transaction automation: MCP links various trading and risk control Agents to address issues like slippage, trade wear and tear, MEV, etc., achieving safer and more efficient on-chain asset management.

Three, Related Projects

1. MC

DeMCP is a decentralized MCP network. It is dedicated to providing self-developed open-source MCP services for AI Agents, offering a deployment platform for MCP developers to share commercial revenue, and achieving one-stop access to mainstream large language models (LLMs). Developers can access services by supporting stablecoins (USDT, USDC). As of May 8th, its token DMCP has a market value of approximately $1.62M.

2.DARK

DARK is a MCP network in a Trusted Execution Environment (TEE), built on Solana. The token $DARK is listed on Binance Alpha, with a market value of approximately 11.81 million US dollars as of May 8th. Currently, DARK’s first application is in the development phase, which will provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access a variety of tools and external services through simple configurations. Although the product has not been fully released, users can join the early experience phase through email registration, participate in testing, and provide feedback.

3.Cookie.fun

Cookie.fun is a platform dedicated to AIAgent in the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analysis tool. The platform helps users understand and evaluate the performance of different AI Agents by showing indicators such as the cognitive influence, intelligent follow-up ability, user interaction, and on-chain data of AI Agents. On April 24th, Cookie.API 1.0 update introduced a dedicated MCP server, which includes plug-and-play intelligent agent-specific MCP servers designed for developers and non-technical personnel, requiring no configuration.

Source of information:X

4.SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at building a blockchain-native AI infrastructure by expanding MCP. The platform provides scalable and interoperable data protocols for Web3-based AI applications, plans to simplify the development process by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby promoting the practical application of AI in the blockchain environment. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with a data volume exceeding 10 billion rows, and will soon launch MCP data servers supporting the Ethereum mainnet and Base Chain. Its token SkyAI is listed on Binance Alpha, with a market value of approximately 42.7 million US dollars as of May 8th.

4. Future Development

The MCP protocol, as a new narrative of the integration of AI and blockchain, has shown great potential in improving data interaction efficiency, reducing development costs, enhancing security and privacy protection, etc., especially in decentralized finance and other scenarios, with broad application prospects. However, most MCP-based projects are still in the conceptual verification stage, without mature products, leading to a continuous decline in token prices after going online. For example, the price of the DeMCP token has dropped by 74% in less than a month after going online. This phenomenon reflects a trust crisis in the market towards MCP projects, mainly due to long product development cycles and a lack of practical application landing. Therefore, how to accelerate product development progress, ensure close connection between tokens and actual products, and improve user experience will be the core issues facing current MCP projects. In addition, the promotion of the MCP protocol in the encrypted ecosystem still faces the challenge of technical integration. Due to differences in smart contract logic and data structures between different blockchains and DApps, a unified and standardized MCP server still requires a significant amount of development resources.

Despite the above challenges, the MCP protocol itself still demonstrates tremendous potential for market development. With the continuous advancement of AI technology and the gradual maturity of the MCP protocol, it is expected to achieve a broader application in the fields of DeFi, DAO, and more. For example, AI agents can obtain on-chain data in real time through the MCP protocol, execute automated transactions, 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 securitization of AI assets. As an important auxiliary force for the integration of AI and blockchain, the MCP protocol is expected to become a crucial engine driving the next generation of AI agents with the continuous maturity of technology and the expansion of application scenarios. However, achieving this vision still requires addressing various challenges such as technological integration, security, and user experience.

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