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

1. Introduction to the Concept of MCP

Traditional chatbots often lack personalized settings, resulting in uniform responses that lack warmth. To address this issue, developers introduced the concept of "character setting," giving AI specific roles and tones. However, even so, AI remains a passive responder and cannot proactively perform complex tasks.

To break through 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 can generate operation instructions based on preset rules and tools, automatically perform 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 non-unified tool calling formats and poor cross-platform compatibility. To address this, the Model Context Protocol (MCP) was born. 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. This protocol significantly simplifies the development process, enabling AI models to interact with external tools more quickly and effectively.

2. Collaboration between MCP and AI Agent

MCP and AI Agent have a complementary relationship. The AI Agent primarily focuses on automated operations in blockchain, execution of smart contracts, and management of crypto assets, emphasizing privacy protection and integration of decentralized applications. In contrast, MCP is centered 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 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-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 a new direction for AI Agents, namely the collaboration of multiple AI Agents. Through MCP, AI Agents can collaborate according to functional divisions, combining to complete complex tasks such as on-chain data analysis, market forecasting, and risk control management, thereby improving overall efficiency and reliability. In terms of on-chain transaction automation, MCP connects various trading and risk control Agents, helping to address issues such as slippage, transaction wear, and MEV in trading, achieving safer and more efficient on-chain asset management.

3. Overview of Related Projects

1. DeMCP: Decentralized MCP network, dedicated to providing self-developed open-source MCP services for AI Agents, offering a deployment platform for developers to share commercial profits, and achieving one-stop access to mainstream large language models.

2. DARK: The MCP network built on Solana runs in a Trusted Execution Environment (TEE). Its first application aims to provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol.

3. Cookie.fun: A platform focused on AI Agents in the Web3 ecosystem, offering comprehensive AI Agent indices and analysis tools. The recent update introduced dedicated MCP servers, featuring plug-and-play smart agent-specific MCP servers designed for developers and non-technical users.

4. SkyAI: A Web3 data infrastructure project built on the BNB Chain, aimed at constructing blockchain-native AI infrastructure by extending MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, supporting multi-chain data access and AI agent deployment.

4. Future Prospects and Challenges

The MCP protocol has demonstrated great potential in improving data interaction efficiency, reducing development costs, enhancing security and privacy protection, particularly in scenarios such as decentralized finance where it has broad application prospects. However, most current projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products, resulting in a trust crisis in the market regarding these projects.

The main challenges include:

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

Despite facing challenges, the MCP protocol itself still shows great potential for market development. 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 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 has the potential to become a key engine driving the next generation of AI Agents. However, realizing this vision still requires addressing challenges in technological integration, security, user experience, and other areas.