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

1. Introduction to MCP Concept

In the field of artificial intelligence, traditional chatbots often lack personalized settings, resulting in monotonous responses. To address this issue, developers have introduced the concept of "character setting," giving AI specific roles and personalities. However, even so, the AI remains a passive responder and cannot actively perform complex tasks.

To break through this limitation, the Auto-GPT project has emerged. It allows developers to define tools and functions for AI, enabling the AI to automatically perform tasks based on preset rules. Although Auto-GPT has achieved a certain degree of autonomous execution, it still faces issues such as non-uniform tool call formats and poor cross-platform compatibility.

In response to these challenges, the MCP (Model Context Protocol) has emerged. The MCP aims to simplify the interaction between AI and external tools by establishing a unified communication standard, allowing AI to easily call various external services. This standardization greatly simplifies the development process, improves efficiency, and enables AI models to interact with external tools more quickly and effectively.

2. The Synergistic Effect 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, is focused on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility.

MCP opens up new development directions for AI Agents, such as multi-AI Agent collaboration. Through MCP, AI Agents can be divided by function to jointly complete complex tasks such as on-chain data analysis, market forecasting, and risk management, enhancing overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents, helping to address issues such as slippage, transaction friction, and MEV, achieving safer and more efficient on-chain asset management.

3. Overview of Related Projects

1. DeMCP: Decentralized MCP network that provides self-developed open-source MCP services for AI Agents and offers a deployment platform for developers to share commercial profits.

2. DARK: The MCP network under the trustworthy execution environment (TEE) built on Solana. 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 within the Web3 ecosystem, providing comprehensive AI Agent indexes and analytical tools. The latest update launched a dedicated MCP server, offering plug-and-play MCP services for developers and non-technical personnel.

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

4. Future Development Prospects

The MCP protocol shows great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in areas like decentralized finance, where it has broad application prospects. However, most projects based on MCP are still in the proof-of-concept stage and have not yet launched mature products, resulting in a downward trend in their token prices.

In the future, the MCP protocol is expected to achieve broader applications in fields such as DeFi and DAO. AI agents can use the MCP protocol to access on-chain data in real-time, execute automated trades, and improve market analysis efficiency and accuracy. The decentralized nature of the MCP protocol provides a transparent and traceable operating platform for AI models, promoting the decentralization and assetization of AI assets.

However, to realize this vision, challenges in various aspects such as technological integration, security, and user experience still need to be addressed. As technology continues to mature and application scenarios expand, the MCP protocol is expected to become an important engine for driving the development of the next generation of AI Agents, paving new development paths for artificial intelligence applications.