A New Intelligent Application Framework Integrating MCP Concepts with AI Agents

Introduction to the MCP Concept

Traditional chatbots in the field of artificial intelligence often rely on generic dialogue models, lacking personalized character settings, resulting in responses that are monotonous and lack human warmth. To address this issue, developers have introduced the concept of "personas," assigning specific roles, personalities, and tones to the AI to make its responses more aligned with user expectations. However, even with rich "personas," the AI 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 within the system. When users make requests, Auto-GPT generates operational instructions based on preset rules and tools, automatically executing tasks and returning results, transforming AI from a passive conversationalist into an active task executor.

Despite the implementation of autonomous execution by Auto-GPT, it still faces issues such as non-unified tool calling formats and poor cross-platform compatibility. To address these issues, the MCP (Model Context Protocol) has emerged. MCP aims to simplify the interaction between AI and external tools, providing a unified communication standard that allows AI to easily call various external services. Traditionally, enabling large-scale models to execute complex tasks requires extensive code and tool descriptions, while the MCP protocol significantly simplifies this process by defining standardized interfaces and communication specifications, thereby improving the efficiency of interactions between AI models and external tools.

The Integration of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on blockchain automation operations, smart contract execution, and cryptocurrency 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.

MCP provides a unified communication standard for AI Agents to interact with external tools (including blockchain data, smart contracts, off-chain services, etc.), solving the problem of fragmented interfaces in traditional development, allowing 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 investment portfolios through MCP.

MCP has also opened new directions for AI Agents: collaboration among 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 forecasting, and risk management, enhancing overall efficiency and reliability. In terms of on-chain transaction automation, MCP connects various trading and risk control Agents to address issues like slippage, transaction friction, and MEV during trading, achieving safer and more efficient on-chain asset management.

Related Projects

1. DeMCP: Decentralized MCP network, providing self-developed open-source MCP services for AI Agents, offering a deployment platform for developers to share commercial profits and achieve one-stop access to mainstream large language models.

2. DARK: MCP network built on a trusted execution environment (TEE) based on Solana. Its first application is under development and will provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols.

3. Cookie.fun: A platform focused on AI Agents within the Web3 ecosystem, providing comprehensive AI Agent indexes and analytical tools. The latest update introduced a dedicated MCP server, including plug-and-play MCP servers specifically for agents.

4. SkyAI: A Web3 data infrastructure project built on the BNB Chain, which constructs a blockchain-native AI infrastructure by extending MCP. Currently supports aggregated datasets from BNB Chain and Solana, and will support MCP data servers for the Ethereum mainnet and Base chain in the future.

Future Development

The MCP protocol, as a new narrative of the fusion of AI and blockchain, demonstrates great potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection. However, most current projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products. Accelerating product development, ensuring that tokens are closely related to actual products, and improving user experience are the core issues faced by current MCP projects.

Despite facing challenges, the MCP protocol itself still demonstrates significant market development potential. With advancements in AI technology and the maturation 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 obtain on-chain data in real time through the MCP protocol, execute automated trading, and enhance the efficiency and accuracy of market analysis. 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.

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