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

Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often lack personalized settings, resulting in uniform responses that lack warmth. To address this issue, developers introduced the concept of "personality settings" to give AI specific roles, personalities, and tones. However, even so, AI remains merely a passive responder and cannot proactively perform complex tasks.

The Auto-GPT project has emerged, allowing developers to define tools and functions for AI and register them in the system. After users make a request, Auto-GPT can generate operation instructions based on predefined rules and tools, automatically execute tasks, and return results, transforming 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 calling formats and poor cross-platform compatibility. To address this, the MCP (Model Context Protocol) has emerged. 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 simplifies the development process, reduces time costs, and improves the efficiency of interaction between AI models and external tools.

The Synergistic Effect of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on the automation of blockchain operations, execution of smart contracts, and management of crypto assets, emphasizing privacy protection and integration of decentralized applications. On the other hand, MCP focuses on simplifying the interaction between 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 (such as 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 their autonomous execution capabilities. For example, DeFi AI Agents can use MCP to access market data in real-time and automatically optimize their investment portfolios.

In addition, MCP has opened up a new direction for AI Agents, namely the collaboration of multiple AI Agents. Through MCP, AI Agents can collaborate according to their functions to jointly 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 transaction automation, MCP can connect 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.

Related Project Overview

1. DeMCP: A decentralized MCP network that provides self-developed open-source MCP services for AI Agents, offers a deployment platform for developers to share commercial profits, and achieves one-stop access to mainstream large language models.

2. DARK: The MCP network built on Solana operates under a Trusted Execution Environment (TEE). Its first application is under development, aiming to provide 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 recent update has launched a dedicated MCP server, offering plug-and-play MCP services specifically for developers and non-technical users.

4. SkyAI: A Web3 data infrastructure project built on the BNB Chain, aiming to create a blockchain-native AI infrastructure by extending the MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications.

Future Development and Challenges

The MCP protocol has shown great potential in the integration of AI and blockchain, particularly in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection. However, most current projects based on MCP are still in the proof-of-concept stage and have not launched mature products, resulting in significant fluctuations in their token prices.

The main challenges include:

1. Accelerate product development progress
2. Ensure a close connection between the tokens and the actual products.
3. Enhance user experience
4. Address the differences in smart contract logic and data structures between different blockchains and DApps.

Despite facing challenges, the MCP protocol still demonstrates great market development potential. With the advancement of AI technology and the 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 trading, and enhance the efficiency and accuracy of market analysis.

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. As an important auxiliary force in the integration of AI and blockchain, the MCP protocol is expected to become a significant engine driving the next generation of AI Agents. However, achieving this vision still requires addressing challenges in technology integration, security, user experience, and other areas.