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 dialogue models, lacking personalized character settings, which leads to monotonous responses that lack warmth. To address this issue, developers have introduced the concept of "character settings" to endow AI with specific roles, personalities, and tones, making its responses more aligned with user expectations. However, even with rich "character settings", AI remains a passive responder, unable to proactively execute tasks or perform complex operations.

To address this limitation, the open-source project Auto-GPT has emerged. It allows 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 executes tasks, and returns results, transforming AI from a passive responder into an active task executor.

Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as non-unified tool invocation formats and poor cross-platform compatibility. To address this, 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, enabling large-scale models to perform complex tasks requires a lot of code and tool documentation, while the MCP protocol significantly simplifies this process by defining standardized interfaces and communication specifications, thereby improving the efficiency of interaction between AI models and external tools.

2. The Integration of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on blockchain automation, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and decentralized application integration. MCP, on the other hand, emphasizes simplifying the interaction between 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, and off-chain services. This standardization resolves the issue of fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect to multi-chain data and tools, significantly enhancing autonomous execution capabilities. For example, DeFi-type AI Agents can use MCP to obtain market data in real-time and automatically optimize their investment portfolios.

In addition, MCP has opened up new directions for AI Agents: collaboration among multiple AI Agents. Through MCP, AI Agents can cooperate with functional division of labor to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby enhancing overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents to address issues such as slippage, trading friction, and MEV in transactions, 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 with shared commercial revenue, and achieving one-stop access to mainstream large language models (LLM). Developers can access services through supported stablecoins.

2. DARK

DARK is an MCP network built on Solana within a trusted execution environment ( TEE ). Its first application is under development, aimed at providing efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access various tools and external services with simple configurations.

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 mental influence, intelligent following ability, user interaction, and on-chain data of AI Agents, helping users understand and evaluate the performance of different AI Agents. Recently, an exclusive MCP server has been launched, featuring plug-and-play AI Agent dedicated MCP servers, designed for developers and non-technical personnel, 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 the MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, with plans to simplify the development process by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, promoting the practical application of AI in blockchain environments. Currently, SkyAI supports aggregated datasets from the BNB Chain and Solana, with data volume exceeding 10 billion rows, and in the future, it will launch MCP data servers supporting the Ethereum mainnet and Base chain.

4. Future Development

The MCP protocol, as a new narrative of the integration of AI and blockchain, shows great potential in enhancing data interaction efficiency, reducing development costs, and improving security and privacy protection, especially in decentralized finance scenarios where it has broad application prospects. However, most current MCP-based projects are still in the proof-of-concept stage and have not launched mature products, resulting in a continuous decline in their token prices after going live. This reflects a crisis of trust in the MCP projects, mainly stemming from the long product development cycles and the lack of practical application implementation.

How to accelerate product development, ensure a close connection between tokens and actual products, and enhance user experience will be the core issues faced by the current MCP project. In addition, the promotion of the MCP protocol in the crypto ecosystem still faces challenges in technical integration. Due to differences in smart contract logic and data structures between different blockchains and DApps, a standardized MCP server still requires a significant investment of development resources.

Despite facing challenges, the MCP protocol itself still demonstrates great 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 access on-chain data in real-time through the MCP protocol to execute automated trades, enhancing 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 for the integration of AI and blockchain, is expected to become a key engine driving the next generation of AI Agents as the technology matures and application scenarios expand. However, achieving this vision still requires addressing challenges in multiple areas such as technology integration, security, and user experience.