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 lack personalized settings, resulting in responses that are single-minded and lack warmth. To address this issue, developers have introduced the concept of "character setting", granting AI specific roles, personalities, and tones. However, even so, the AI remains merely a passive responder, unable to proactively execute tasks or perform complex operations.

To transform AI from a passive conversationalist into an active task executor, the open-source project Auto-GPT has emerged. It allows developers to define tools and functions for the AI and register them within the system. When users make requests, Auto-GPT generates operational instructions based on predefined rules and tools, automatically executing tasks and returning results.

Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as non-uniform tool calling formats and poor cross-platform compatibility. To address these challenges, 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 call various external services.

Traditionally, to enable large-scale models to perform complex tasks (such as querying the weather or accessing web pages), developers needed to write a substantial amount of code and tool specifications, significantly increasing the difficulty and time costs of development. The MCP protocol simplifies this process significantly by defining standardized interfaces and communication protocols, allowing AI models to interact with external tools more quickly and effectively.

2. The Integration of MCP and AI Agent

The relationship between MCP and AI Agent is complementary. The AI Agent mainly focuses on automated operations on the blockchain, execution of smart contracts, and management of crypto assets, emphasizing privacy protection and integration of decentralized applications. MCP, on the other hand, focuses more on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, thereby enhancing cross-platform interoperability and flexibility.

The core value of MCP lies in providing a unified communication standard for the interaction between AI Agents and external tools (including blockchain data, smart contracts, off-chain services, etc.). This standardization solves the problem of fragmented interfaces in traditional development, allowing 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 their portfolios through MCP.

In addition, MCP opens up a new direction for AI Agents, namely collaboration among multiple AI Agents. Through MCP, AI Agents can collaborate based on functional division of labor, combining 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 trading automation, MCP connects various trading and risk control Agents, helping 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 to share in commercial profits, and achieving one-stop access to mainstream large language models (LLM). Developers can access the service through supported stablecoins.

2. DARK

DARK is an MCP network built on Solana in a trusted execution environment ( TEE ). Its first application is in the development stage and aims to provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access various tools and external services through simple configurations.

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 the mental influence of AI Agents, intelligent following capabilities, user interaction, and on-chain data.

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 by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby promoting the practical application of AI in the blockchain environment.

Four, Future Development

The MCP protocol, as a new narrative integrating AI and blockchain, shows great potential in improving data interaction efficiency, reducing development costs, enhancing security, and protecting privacy, especially in decentralized finance scenarios where it has broad application prospects. However, most current 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 phenomenon reflects a trust crisis in the market regarding MCP projects, primarily stemming from the lengthy product development cycle and the lack of practical application.

Therefore, how to accelerate the development progress of the products, ensure a close connection between the tokens and the 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 cryptocurrency ecosystem still faces challenges in technical integration. Due to the differences in smart contract logic and data structures between different blockchains and DApps, a unified standardized MCP server will still require a significant amount of development resources.

Despite facing the aforementioned challenges, the MCP protocol itself still demonstrates enormous market development potential. 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. 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 process 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 with the continuous maturation of technology and the expansion of application scenarios. However, achieving this vision still requires addressing challenges in areas such as technology integration, security, and user experience.