The Integration of MCP Concepts and AI Agents: A New Framework for Artificial Intelligence Applications

Introduction to MCP Concept

In the field of artificial intelligence, traditional chatbots often rely on general dialogue models, lacking personalized settings, which leads to uniform responses that lack human touch. To address this issue, developers introduced the concept of "character setting," assigning specific roles, personalities, and tones to the AI, making its responses closer to user expectations. However, even with rich "character settings," AI remains a passive responder, unable to proactively execute tasks or perform complex operations.

To this end, the Auto-GPT project 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 operational instructions based on preset rules and tools, automatically executing tasks and returning results, transforming AI from a passive respondent to an active task executor.

Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as non-standardized tool invocation formats and poor cross-platform compatibility. To address these problems, 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.

The Integration of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent mainly 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.

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 addresses the issue 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.

In addition, MCP has opened up a new direction for AI Agents, namely collaboration among multiple AI Agents. Through MCP, AI Agents can collaborate according to their functions to complete complex tasks such as on-chain data analysis, market prediction, 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, achieving safer and more efficient on-chain asset management.

Related Projects

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 revenue-sharing opportunities, and achieving one-stop access to mainstream large language models (LLM). Developers can access services by supporting stablecoins.

DARK

DARK is an MCP network built on Solana within a trusted execution environment ( TEE ). Its first application is under development and will 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 configurations.

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 showcases metrics such as the mental influence, intelligent following capability, user interaction, and on-chain data of AI Agents, helping users understand and evaluate the performance of different AI Agents.

SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to construct blockchain-native AI infrastructure by expanding the 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 by integrating multi-chain data access, AI agent deployment, and protocol-level utilities.

Future Development

The MCP protocol, as a new narrative of the fusion of AI and blockchain, has shown great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in scenarios like decentralized finance where it has broad application prospects. However, most of the current projects based on MCP 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 phenomenon reflects a trust crisis in the market regarding the MCP project, primarily stemming from the long product development cycle and lack of practical application implementation. Therefore, how to accelerate product development progress, ensure a close connection between the token and the actual product, and enhance user experience will be the core issues currently faced by the MCP project. In addition, the promotion of the MCP protocol within 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 unified standardized MCP server still requires significant development resources.

Despite facing the aforementioned challenges, the MCP protocol itself demonstrates significant 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 obtain on-chain data in real-time through the MCP protocol, 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 operational 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 crucial 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 areas such as technical integration, security, and user experience.