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

1. The Concept of MCP

In the field of artificial intelligence, traditional chatbots often lack personalization and warmth. To address this issue, developers have introduced the concept of "persona", giving AI specific roles and personalities. However, even with rich "personas", AI remains just a passive responder and cannot proactively execute complex tasks.

To enable AI to become an active task executor, the open-source project Auto-GPT has emerged. It allows developers to define tools and functions for AI, enabling it to automatically perform tasks according to preset rules. However, Auto-GPT still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility.

To address these challenges, the Model Context Protocol (MCP) 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. This greatly reduces development difficulty and time costs, enabling AI models to interact with external tools more quickly and efficiently.

2. The Combination of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent focuses on blockchain operations, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and decentralized application integration. In contrast, MCP focuses on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management to enhance cross-platform interoperability.

MCP provides a unified standard for AI Agents to interact with external tools (such as blockchain data, smart contracts, off-chain services, etc.). This addresses the issue of interface fragmentation 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 use MCP to obtain market data in real-time and automatically optimize their portfolios.

In addition, MCP has opened up new directions for AI Agents: collaboration among multiple AI Agents. Through MCP, AI Agents can be divided by function to collaboratively complete complex tasks such as on-chain data analysis, market forecasting, and risk management, enhancing overall efficiency and reliability. In terms of on-chain transaction automation, MCP can connect various trading and risk control Agents, addressing issues such as slippage, transaction friction, and MEV during trading, achieving safer and more efficient on-chain asset management.

3. Related Projects

1. DeMCP

DeMCP is 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 enables one-stop access to mainstream large language models. Developers can obtain services by supporting stablecoins.

2. DARK

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

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing AI Agent indices and analytical tools. The platform showcases metrics such as the cognitive influence, intelligent following capabilities, user interactions, and on-chain data of AI Agents, helping users evaluate the performance of different AI Agents.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at constructing a blockchain-native AI infrastructure by expanding MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, simplifying the development process and promoting the practical application of AI in a blockchain environment.

4. Future Development

The MCP protocol has demonstrated great potential in improving data exchange efficiency, reducing development costs, and enhancing security and privacy protection, 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 yet launched mature products, resulting in a decline in their token prices. This reflects a trust crisis in the market for MCP projects, primarily stemming from the long product development cycle and the lack of practical application implementation.

In the future, the MCP protocol is expected to achieve broader applications in fields such as DeFi and DAO. AI agents can access on-chain data in real-time through the MCP protocol to execute automated trades, enhancing market analysis efficiency and accuracy. 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.

However, achieving this vision still requires addressing challenges in various aspects such as technology integration, security, and user experience. As technology continues to mature and application scenarios expand, the MCP protocol is expected to become an important engine driving the next generation of AI Agents, but many obstacles still need to be overcome to realize its full potential.