The Integration of MC and AI Agent: A New Paradigm for Artificial Intelligence Applications

The field of artificial intelligence has long faced a challenge: how to enable AI to possess stronger personalization and execution capabilities. Traditional chatbots often lack distinct character traits, responding in a singular manner that lacks warmth. To address this issue, developers have introduced the concept of "personas," giving AI specific roles and tones. However, even with rich "personas," AI remains merely a passive responder, unable to proactively execute complex tasks.

To break through this limitation, Auto-GPT has emerged. It allows developers to define a series of tools and functions for AI, enabling it to automatically execute tasks and return results based on predefined rules. This innovation transforms AI from a passive conversationalist into an active task executor. However, Auto-GPT still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility.

In response to these challenges, the MCP( Model Context Protocol was born. MCP aims to simplify the interaction between AI and external tools by providing a unified communication standard, enabling AI to easily call various external services. This greatly reduces development difficulty and time costs, allowing AI models to interact more efficiently with external tools.

MCP and AI Agent complement each other. The AI Agent mainly focuses on blockchain operations, smart contract execution, and cryptocurrency asset management, while MCP is dedicated to simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management. The introduction of MCP significantly enhances the execution capability and flexibility of the AI Agent.

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For example, in the DeFi sector, AI Agents can obtain market data in real time and automatically optimize portfolios through MCP. MCP also opens up new avenues for collaboration among multiple AI Agents, allowing them to work together to complete complex on-chain data analysis, market forecasting, and risk management tasks. In addition, MCP helps achieve safer and more efficient on-chain asset management, addressing issues such as slippage, wear, and MEV in transactions.

Currently, there are some projects based on the MCP concept in the market:

1. DeMCP: A decentralized MCP network that provides self-researched open-source MCP services for AI Agents and achieves one-stop access to mainstream large language models.

2. DARK: The MCP network built on Solana operates in a trusted execution environment. Its first application is under development, aimed at providing efficient tool integration capabilities for AI Agents.

3. Cookie.fun: A platform focused on AI Agents in the Web3 ecosystem, providing comprehensive AI Agent indices and analytical tools. The latest update introduced dedicated MCP servers, making it easier for developers to integrate quickly.

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

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Although the MCP protocol has shown great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, most current projects based on MCP are still in the proof-of-concept stage. This has led to a continuous decline in their token prices after launch, reflecting a crisis of trust in the MCP projects within the market.

In the future, the development of the MCP protocol still faces many challenges, including accelerating product development progress, ensuring a close connection between tokens and actual products, and improving user experience. Technical integration is also a major challenge, as there are differences in smart contract logic and data structures between different blockchains and DApps.

Nevertheless, the MCP protocol still demonstrates great market development potential. With the advancement of AI technology and the maturity of the MCP protocol, it is expected to achieve wider applications in areas such as DeFi and DAO in the future. 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.

Overall, the MCP protocol, as an important auxiliary force in the integration of AI and blockchain, is expected to become a vital engine driving the next generation of AI Agents. However, achieving this vision still requires addressing challenges in various aspects such as technical integration, security, and user experience. As these issues are gradually resolved, the MCP protocol will play an increasingly important role in the new paradigm of artificial intelligence applications.

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