1. Introduction to MCP Concept
In the field of artificial intelligence, traditional chatbots relied heavily on general dialogue models, lacking personalized character settings, leading to responses that often seemed singular and lacked warmth. To address this issue, developers introduced the concept of 'character setting', assigning specific roles, personalities, and tones to AI to make responses closer to user expectations. However, even with a rich 'character setting', AI remains merely a passive responder, unable to proactively perform tasks or carry out complex operations. Hence, the open-source project Auto-GPT emerged. Auto-GPT allows developers to define a series of tools and functions for AI and register these tools within the system. When a user makes a request, Auto-GPT generates corresponding operational instructions based on predefined rules and tools, automatically executing tasks and returning results. This approach transforms AI from a passive conversationalist into an active task-oriented AI.
Although Auto-GPT has achieved a certain level of autonomous execution for AI, it still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility. To address these problems, MCP (Model Context Protocol) was developed, aimed at solving the main challenges faced by AI during development, 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, allowing AI to easily invoke various external services. Traditionally, to enable large-scale models to perform complex tasks (such as querying the weather or accessing web pages), developers had to write a lot of code and tool descriptions, significantly increasing development difficulty and time costs. The MCP protocol simplifies this process by defining standardized interfaces and communication specifications, allowing AI models to interact more quickly and effectively with external tools.
2. Integration of MCP and AI Agents
MCP and cryptographic AI Agents are complementary to each other. The difference between them lies in the fact that AI Agents primarily focus on automated operations on the blockchain, smart contract execution, and cryptographic asset management, emphasizing privacy protection and the integration of decentralized applications. MCP focuses more on simplifying the interaction between AI Agents and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility. Cryptographic AI Agents can achieve more efficient cross-platform integration and operations through the MCP protocol, thereby enhancing their execution capabilities.
Previous AI Agents had certain execution capabilities, such as executing trades through smart contracts and managing wallets. However, these functions were usually predefined, lacking 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 fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing their autonomous execution capabilities. For example, DeFi AI Agents can use MCP to obtain market data in real-time and automatically optimize their portfolios. Furthermore, MCP opens up a new direction for AI Agent collaboration: through MCP, AI Agents can collaborate by function, combining to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, improving overall efficiency and reliability. On-chain transaction automation: MCP connects various trading and risk control Agents, addressing issues like slippage, transaction wear, and MEV, achieving safer and more efficient on-chain asset management.
3. Related Projects
1.DeMCP
DeMCP is a decentralized MCP network. It is committed to providing self-developed open-source MCP services for AI Agents, offering a deployment platform for MCP developers with shared commercial benefits, and achieving one-stop access to mainstream large language models (LLMs). Developers can obtain services by supporting stablecoins (USDT, USDC). As of May 8, its token DMCP has a market value of approximately $1.62M.
2.DARK
DARK is an MCP network built on Solana within a trusted execution environment (TEE). The token $DARK was launched on Binance Alpha, with a market value of approximately 11.81 million USD as of May 8. Currently, the first application of DARK is in the development stage, which will provide AI Agents with efficient tool integration capabilities through TEE and the MCP protocol, allowing developers to quickly access various tools and external services through simple configurations. Although the product has not been fully released, users can join the early access phase by signing up via email to participate in testing and provide feedback.
3.Cookie.fun
Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analysis tools. The platform helps users understand and evaluate the performance of different AI Agents by showcasing metrics such as the cognitive influence of AI Agents, smart following capabilities, user interaction, and on-chain data. On April 24, the Cookie.API 1.0 update launched a dedicated MCP server, which includes plug-and-play intelligent agent-specific MCP servers designed for developers and non-technical users without any configuration required.
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4.SkyAI
SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to create blockchain-native AI infrastructure by extending MCP. The platform provides scalable and interoperable data protocols for Web3-based AI applications, planning 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 blockchain environments. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with data volume exceeding 10 billion rows, and will soon launch MCP data servers supporting Ethereum mainnet and Base chain. Its token SkyAI was launched on Binance Alpha, with a market value of approximately 42.7 million USD as of May 8.
4. Future Development
The MCP protocol, as a new narrative of the integration of AI and blockchain, shows great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in decentralized finance scenarios, with broad application prospects. However, most current MCP-based projects are still in the proof-of-concept stage and have not yet launched mature products, leading to a continuous decline in their token prices after launch. For example, the price of the DeMCP token has fallen by 74% within less than a month of its launch. This phenomenon reflects a market trust crisis in MCP projects, mainly stemming from long product development cycles and a lack of practical application. Therefore, accelerating product development, ensuring a close connection between tokens and actual products, and enhancing user experience will be core issues faced by current MCP projects. Additionally, promoting the MCP protocol within the crypto ecosystem still faces challenges of 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 a significant investment of development resources.
Despite the challenges mentioned above, the MCP protocol itself still shows great potential for market development. With the continuous advancement of AI technology and the gradual maturity of the MCP protocol, it is expected to achieve broader applications in fields such as DeFi and DAO in the future. For example, AI agents can use the MCP protocol to obtain on-chain data in real-time, execute automated trading, and improve the efficiency and accuracy of market analysis. Additionally, the decentralized nature of the MCP protocol is expected to provide AI models with a transparent and traceable operating platform, 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 crucial engine driving the next generation of AI Agents as technology matures and application scenarios expand. However, achieving this vision still requires addressing various challenges such as technical integration, security, and user experience.
