I. Introduction to MCP Concept
Previously in the artificial intelligence field, traditional chatbots heavily relied on generic dialogue models, lacking personalized character settings, which resulted in monotonous and impersonal responses. To address this issue, developers introduced the concept of "persona", giving AI specific roles, personalities, and tones to make responses more aligned with user expectations. However, even with a rich "persona", AI remained merely a passive responder, unable to proactively execute tasks or perform complex operations. Therefore, 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 in the system. When a user makes a request, Auto-GPT generates corresponding operational instructions based on preset rules and tools, automatically executing tasks and returning results. This approach transforms AI from a passive conversationalist to an active task AI.
Although Auto-GPT achieved a certain degree of autonomous execution for AI, it still faced issues such as inconsistent tool calling formats and poor cross-platform compatibility. To solve these problems, MCP (Model Context Protocol) was born, aiming to address the primary challenges AI faces during development, especially the complexity of integrating with external tool sets. The core goal of MCP is to simplify AI's interaction with external tools by providing a unified communication standard, enabling AI to easily call various external services. Traditionally, to enable large-scale models to perform complex tasks (such as querying weather or accessing web pages), developers needed to write extensive code and tool descriptions, significantly increasing development difficulty and time costs. The MCP protocol, by defining standardized interfaces and communication specifications, significantly simplifies this process, allowing AI models to interact with external tools more quickly and effectively.
II. Integration of MCP and AI Agent
MCP and crypto AI Agents have a complementary relationship. The difference is that AI Agents primarily focus on blockchain automation, smart contract execution, and crypto asset management, emphasizing privacy protection and decentralized application integration. MCP focuses more on simplifying AI Agent interactions with external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility. Crypto AI Agents can achieve more efficient cross-platform integration and operation through the MCP protocol, thereby enhancing their execution capabilities.
Previous AI Agents had certain execution capabilities, such as executing transactions through smart contracts and managing wallets. However, these functions were typically predefined, lacking flexibility and adaptability. The core value of MCP lies in providing a unified communication standard for AI Agents' interactions with external tools (including blockchain data, smart contracts, off-chain services, etc.). This standardization resolves the interface fragmentation issues in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing AI Agents' autonomous execution capabilities. For example, DeFi AI Agents can obtain real-time market data and automatically optimize portfolios through MCP. Additionally, MCP opens up a new direction for AI Agents: multi-agent collaboration. Through MCP, AI Agents can collaborate by division of labor, combining to complete complex tasks such as on-chain data analysis, market prediction, and risk management, improving overall efficiency and reliability. On-chain transaction automation: MCP connects various trading and risk management Agents, solving issues like slippage, transaction wear, and MEV, achieving safer and more efficient on-chain asset management.
III. Related Projects
1. DeMCP
DeMCP is a decentralized MCP network. It is dedicated to providing self-developed open-source MCP services for AI Agents, offering a deployment platform with commercial revenue sharing for MCP developers, and achieving one-stop access for mainstream large language models (LLM).Developers can obtain services by supporting stablecoins (USDT, USDC). As of May 8th, its token DMCP has a market cap of approximately $1.62M.
2. DARK
DARK is an MCP network built on Solana, operating in a Trusted Execution Environment (TEE). The token $DARK is listed on Binance Alpha, with a market cap of approximately $11.81 million as of May 8th. Currently, DARK's first application is under development, 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 configuration. Although the product has not been fully released, users can join the early experience phase through email waitlist, participating in testing and providing feedback.
3. Cookie.fun
Cookie.fun is a platform focused on AI Agents in the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analysis tool.The platform helps users understand and evaluate different AI Agents' performance by showcasing metrics such as mental influence, intelligent following ability, user interaction, and on-chain data. On April 24th, Cookie.API 1.0 updated and launched a dedicated MCP server, which includes a plug-and-play MCP server for intelligent agents, designed for developers and non-technical personnel without any configuration requirements.
Information source: X
4. SkyAI
SkyAI is a Web3 data infrastructure project built on BNB Chain, aiming to construct a blockchain-native AI infrastructure by extending MCP. The platform provides a scalable and interoperable data protocol for Web3 AI applications, planning to simplify the development process by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby promoting AI's practical applications in blockchain environments. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with data exceeding 10 billion rows, and plans to launch MCP data servers supporting Ethereum mainnet and Base chain in the future. Its token SkyAI is listed on Binance Alpha, with a market cap of approximately $42.7 million as of May 8th.
IV. Future Development
The MCP protocol, as a new narrative of AI and blockchain integration, demonstrates enormous potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in decentralized finance scenarios. However, most MCP-based projects are currently in the proof-of-concept stage, lacking mature products, which has led to continuous token price declines after listing, such as the DeMCP token dropping 74% within a month of listing. This phenomenon reflects market trust issues, primarily stemming from lengthy product development cycles and lack of actual application implementation. Therefore, accelerating product development, ensuring close association between tokens and actual products, and improving user experience are the core challenges facing current MCP projects. Additionally, promoting the MCP protocol in the crypto ecosystem still faces technical integration challenges. Due to differences in smart contract logic and data structures across different blockchains and DApps, developing a unified standardized MCP server requires significant development resources.
Despite these challenges, the MCP protocol itself still shows enormous market development potential. With continuous AI technology advancements and MCP protocol maturation, broader applications are expected in areas like DeFi and DAOs in the future. For example, AI agents can obtain real-time on-chain data through the MCP protocol to execute automated trades, improving market analysis efficiency and accuracy. Moreover, the decentralized nature of the MCP protocol may provide a transparent, traceable platform for AI models, promoting the decentralization and asset-ization of AI assets. As an important auxiliary force in AI and blockchain integration, the MCP protocol, with continuous technological maturation and application scenario expansion, has the potential to become a crucial engine for the next generation of AI Agents. However, realizing this vision still requires addressing challenges in technical integration, security, and user experience.
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