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

1. Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often lack personalization and human touch. To address this issue, developers have introduced the concept of "character settings," giving AI specific roles, personalities, and tones. However, even with rich "character settings," AI remains merely a passive responder and cannot actively perform complex tasks.

To enable AI to actively perform tasks, the Auto-GPT project was born. It allows developers to define tools and functions for AI, enabling it to automatically execute tasks and return results based on preset rules. 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 these challenges, the MCP (Model Context Protocol) has emerged. MCP aims to simplify the interaction between AI and external tools by providing a unified communication standard that enables AI to easily call various external services. This standardized interface and communication specification significantly streamline the development process, allowing AI models to interact with external tools more quickly and effectively.

2. The Collaboration Between MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on automated operations in blockchain, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and the integration of decentralized applications. On the other hand, MCP focuses on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, which enhances 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 collaboration among AI Agents. Through MCP, multiple AI Agents can collaborate according to functional division of labor to jointly complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby improving 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 wear, and MEV in trading, achieving safer and more efficient on-chain asset management.

3. Related Projects

1. DeMCP

DeMCP is a decentralized MCP network dedicated to providing self-researched open-source MCP services for AI Agents, offering a deployment platform that shares commercial profits with MCP developers, and achieving one-stop access to mainstream large language models (LLM). Developers can access services through support for stablecoins.

2. DARK

DARK is an MCP network built on Solana under a Trusted Execution Environment ( TEE ). Its first application is under development, aiming to provide AI Agents with efficient tool integration capabilities through TEE and the MCP protocol, allowing developers to quickly access various tools and external services with simple configurations.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents in the Web3 ecosystem, providing users with comprehensive AI Agent indices and analysis tools. The platform helps users understand and evaluate the performance of different AI Agents by showcasing indicators such as the mental influence, intelligent following capability, user interaction, and on-chain data of AI Agents. Recently, the Cookie.API1.0 update introduced dedicated MC servers, which include plug-and-play MCP servers specifically designed for developers and non-technical personnel, requiring no configuration.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to construct blockchain-native AI infrastructure by expanding MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, planning to streamline the development process by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby promoting the practical application of AI in a blockchain environment. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with over 10 billion rows of data, and plans to launch MCP data servers supporting the Ethereum mainnet and Base chain in the future.

4. Future Development

The MCP protocol has shown great potential in improving data exchange efficiency, reducing development costs, enhancing security, and protecting privacy, especially in decentralized finance scenarios where it has a wide range of application prospects. However, most current projects based on MCP are still in the proof-of-concept stage and have not launched mature products, resulting in a continuous decline in their token prices after going live. This phenomenon reflects a trust crisis in MCP projects in the market, mainly stemming from the long product development cycle and the lack of practical application.

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

However, achieving this vision still requires addressing challenges in various aspects such as technical integration, security, and user experience. How to accelerate product development, ensure a close link between the token and the actual product, and enhance user experience will be the core issues faced by the current MCP project. At the same time, due to differences in smart contract logic and data structures between different blockchains and DApps, a standardized MCP server will still require a significant investment of development resources.

Overall, the MCP protocol, as an important auxiliary force in the integration of AI and blockchain, is expected to become a significant engine driving the next generation of AI Agents with the continuous maturation of technology and the expansion of application scenarios. However, achieving this goal still requires overcoming numerous challenges and ongoing efforts and innovations from the industry.