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 rely on general conversation models, lacking personalized role settings, which leads to uniform responses that lack warmth. To address this issue, developers have introduced the concept of "character setting," endowing AI with specific roles, personalities, and tones, making its responses closer to user expectations. However, even if the AI has a rich "character setting," it remains a passive responder, unable to proactively execute tasks or perform complex operations.

The Auto-GPT project has emerged, allowing developers to define tools and functions for AI and register them in the system. When users make requests, Auto-GPT generates operation instructions based on preset rules and tools, automatically executing tasks and returning results, transforming AI from a passive respondent into an active task executor.

Despite the fact that 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 this, the Model Context Protocol (MCP) has emerged, aiming to tackle key challenges in the AI development process, especially the complexity involved in integrating with external tools. The core objective of MCP is to simplify the interaction between AI and external tools by providing a unified communication standard, allowing AI to easily invoke a variety of external services. Traditionally, enabling large-scale models to execute complex tasks requires writing extensive code and tool documentation, 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 with external tools more quickly and effectively.

2. The Integration of MCP and AI Agent

MCP and the crypto AI Agent complement each other. 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 crypto AI Agent can achieve more efficient cross-platform integration and operations through the MCP protocol, improving execution capabilities.

Previously, AI Agents had certain execution capabilities, such as executing transactions 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 (such as blockchain data, smart contracts, off-chain services, etc.). This standardization resolves the issue of fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing autonomous execution capabilities. For example, DeFi-type AI Agents can obtain market data in real-time and automatically optimize their portfolios through MCP.

MCP has opened up a new direction for AI Agents, which is the collaboration of multiple AI Agents: through MCP, AI Agents can collaborate according to their functional divisions to complete complex tasks such as on-chain data analysis, market prediction, and risk control management, thereby enhancing overall efficiency and reliability. In terms of on-chain transaction automation, MCP connects various trading and risk control Agents to address issues such as slippage, transaction wear, 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 to share commercial profits, and enabling one-stop access to mainstream large language models (LLM). Developers can obtain services through supported stablecoins. As of May 8, its token DMCP has a market value of approximately $1.62M.

2. DARK

DARK is a trustworthy execution environment built on Solana under the TEE( of the MCP network. Its first application is currently under development and 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 configuration. The product has not yet been fully released, and users can join the early experience phase through 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 metrics such as their mental influence, intelligent following ability, user interaction, and on-chain data. On April 24th, the Cookie.API 1.0 update introduced exclusive MCP servers, which include plug-and-play MCP servers specifically for agents, designed for both developers and non-technical users with no configuration required.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at constructing blockchain-native AI infrastructure through the expansion of MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, planning to simplify the development process and promote the practical application of AI in blockchain environments through the integration of multi-chain data access, AI agent deployment, and protocol-level utilities. 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 Ethereum mainnet and Base chain.

4. Future Development

The MCP protocol, as a new narrative integrating AI and blockchain, demonstrates huge potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, particularly in decentralized finance scenarios where it has broad application prospects. However, most 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 crisis of trust in the market regarding MCP projects, mainly stemming from long product development cycles and a lack of real-world application. Therefore, accelerating product development, ensuring a close connection between tokens and actual products, and enhancing user experience will be the core issues facing current MCP projects. Moreover, the promotion of the MCP protocol in the crypto ecosystem still faces technical integration challenges. Due to differences in smart contract logic and data structures between different blockchains and DApps, a standardized MCP server still requires a substantial investment of development resources.

Despite facing the aforementioned challenges, the MCP protocol itself still demonstrates 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 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 trades, and enhance the efficiency and accuracy of market analysis. Additionally, 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 in the integration of AI and blockchain, is expected to become a significant engine driving the next generation of AI Agents as technology continues to mature and application scenarios expand. However, realizing this vision still requires addressing challenges in areas such as technology integration, security, and user experience.

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