MCP and AI Agent: A New Framework for Artificial Intelligence Applications

1. Introduction to the Concept of MCP

In the field of artificial intelligence, traditional chatbots often rely on generic conversational models, lacking personalized settings, which leads to monotonous and bland responses. To address this issue, developers have introduced the concept of "personality", endowing AI with specific roles, characteristics, and tones, making its responses more aligned with user expectations. However, even with a rich "personality", AI remains a passive responder, unable to proactively perform tasks or carry out complex operations.

To overcome this limitation, the Auto-GPT project was born. It allows developers to define tools and functions for AI and register them in the system. When users make requests, Auto-GPT generates operational instructions based on preset rules and tools, automatically executes tasks, and returns results. This transforms AI from a passive conversationalist into an active task executor.

Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility. To address these challenges, the MCP (Model Context Protocol) has emerged. MCP aims 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, developers needed to write a significant amount of code and tool documentation, greatly increasing development difficulty and time costs. The MCP protocol simplifies this process significantly by defining standardized interfaces and communication specifications, enabling AI models to interact with external tools more quickly and effectively.

2. The Collaborative Relationship between MCP and AI Agent

MCP and AI Agent have a complementary relationship. The AI Agent mainly focuses on blockchain automation, 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 to enhance cross-platform interoperability and flexibility.

Traditional AI Agents have certain execution capabilities, such as executing transactions 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 the interaction of AI Agents with external tools (including blockchain data, smart contracts, off-chain services, etc.). This standardization addresses the issue of interface fragmentation in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing their autonomous execution capabilities.

For example, DeFi-type AI Agents can obtain market data in real-time and automatically optimize portfolios through MCP. In addition, MCP opens up a new direction for AI Agents, which is collaboration among multiple AI Agents: through MCP, AI Agents can collaborate based on functional division of labor to complete complex tasks such as on-chain data analysis, market prediction, and risk management, thereby improving overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents, addressing issues such as slippage, trading friction, and MEV during transactions, achieving safer and more efficient on-chain asset management.

3. Introduction to 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 with shared commercial benefits, and achieving one-stop access to mainstream large language models. Developers can access services by using stablecoins.

2. DARK

DARK is a trusted execution environment built on Solana under the MCP network (TEE). Its first application is currently in development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol, allowing developers to quickly access various tools and external services with simple configuration.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing users with comprehensive AI Agent indices and analytical tools. The platform showcases metrics such as the cognitive influence, intelligent following capabilities, user interactions, and on-chain data of AI Agents, helping users understand and evaluate the performance of different AI Agents. Recently, Cookie.fun launched a dedicated MCP server, which includes plug-and-play AI Agent-specific MCP servers designed for both developers and non-technical users, requiring no configuration.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at constructing blockchain-native AI infrastructure by expanding MCP. The platform provides scalable and interoperable data protocols for Web3-based AI applications, planning to simplify the development process and promote the practical applications 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, and will later launch MCP data servers supporting the Ethereum mainnet and Base chain.

4. Future Development Prospects

The MCP protocol, as a new narrative of the integration of AI and blockchain, has demonstrated great potential in improving data interaction efficiency, reducing development costs, enhancing security, and protecting privacy, particularly in scenarios such as decentralized finance where there are broad application prospects. However, most of the current projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products, resulting in a continuous decline in their token prices after launch. This reflects a crisis of trust in the market regarding MCP projects, primarily stemming from the long product development cycles and the lack of practical application.

Therefore, accelerating the product development pace, ensuring a close connection between the token and the actual product, and enhancing the user experience have become the core issues facing the current MCP project. In addition, the promotion of the MCP protocol within the crypto ecosystem still faces challenges of technical integration. Due to the differences in smart contract logic and data structures between different blockchains and DApps, a standardized MCP server still requires significant development resources.

Despite facing challenges, the MCP protocol itself still demonstrates immense market development potential. With the continuous advancement of AI technology and the gradual 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 use the MCP protocol to access on-chain data in real-time, execute automated trading, and enhance the efficiency and accuracy of market analysis. Furthermore, 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 key engine for driving the next generation of AI Agents as technology matures and application scenarios expand. However, achieving this vision still requires addressing challenges in multiple areas such as technical integration, security, and user experience.