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

MCP Concept Analysis

Traditional chatbots in the field of artificial intelligence rely heavily on generic dialogue models, lacking personalized settings, which leads to monotonous and tedious responses. To address this issue, developers introduced the concept of "character setting," endowing AI with specific roles, personalities, and tones, making its responses closer to user expectations. However, even so, AI remains just a passive responder, unable to proactively execute tasks or perform complex operations.

To overcome this limitation, the Auto-GPT project was born. It allows developers to define tools and functions for AI and register them in the system. When users make requests, Auto-GPT generates operation instructions based on preset rules and tools, automatically executes tasks, and returns 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, MCP (Model Context Protocol) has emerged, aiming to simplify the interaction between AI and external tools. By providing a unified communication standard, MCP enables AI to easily call various external services, significantly simplifying the development process and improving efficiency.

Collaboration between MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent primarily focuses on automation operations in blockchain, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and decentralized application integration. On the other hand, MCP focuses on simplifying the interaction between the 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 problem of fragmented interfaces in traditional development, enabling 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 multi-Agent collaboration. Through MCP, AI Agents can collaborate based on functional division of labor to jointly complete complex tasks such as on-chain data analysis, market forecasting, and risk control management, improving overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents to address issues such as slippage, trading friction, and MEV during transactions, achieving safer and more efficient on-chain asset management.

Related Projects

DeMCP

DeMCP is a decentralized MCP network dedicated to providing self-developed open-source MCP services for AI Agents, offering a deployment platform for MCP developers to share commercial revenues, and achieving one-stop access to mainstream large language models. Developers can access services using stablecoins. As of May 8, its token DMCP has a market capitalization of approximately 1.62 million USD.

DARK

DARK is a trusted execution environment based on Solana, under the MCP network of ( TEE ). Its first application is currently under development, aimed at providing AI Agents with efficient tool integration capabilities through TEE and MCP protocols, allowing developers to quickly access various tools and external services with simple configurations. Currently, users can join the early experience phase through an email waitlist.

Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing users with comprehensive AI Agent indices and analysis tools. The platform showcases metrics such as the mental influence of AI Agents, intelligent following capabilities, user interactions, and on-chain data, helping users assess the performance of different AI Agents. On April 24th, the Cookie.API 1.0 update launched a dedicated MCP server, including plug-and-play MCP servers specifically for agents, designed for developers and non-technical users, requiring no configuration.

SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at constructing blockchain-native AI infrastructure by extending the MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, planning to simplify the development process through the integration of multi-chain data access, AI agent deployment, and protocol-level utilities. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, with over 10 billion rows of data, and will also support MCP data servers from the Ethereum mainnet and Base chain in the future.

Future Outlook

The MCP protocol, as a new narrative for the integration of AI and blockchain, demonstrates great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in decentralized finance and other scenarios with 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, leading to a continuous decline in their token prices after going live. This reflects a crisis of trust in the MCP projects in the market, primarily due to the lengthy product development cycles and the lack of practical application implementation.

Therefore, how to accelerate product development progress, ensure a close connection between the token and the actual product, and enhance user experience will be the core issues currently faced by the MCP project. In addition, the promotion of the MCP protocol in the crypto ecosystem still faces challenges in technical integration. Due to the differences in smart contract logic and data structures between different blockchains and DApps, a unified standardized MCP server still requires a significant investment of development resources.

Despite facing challenges, the MCP protocol itself still demonstrates immense market development potential. With the continuous advancement of AI technology and the gradual 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 use the MCP protocol to access on-chain data in real-time, execute automated trading, and enhance the efficiency and accuracy of market analysis. Furthermore, the decentralized characteristics of the MCP protocol are expected to provide a transparent and traceable operating platform for AI models, promoting the decentralization and assetization process of AI assets.

The MCP protocol, as an important auxiliary force for the integration of AI and blockchain, is expected to become a key engine driving the next generation of AI Agents with the continuous maturity of technology and the expansion of application scenarios. However, achieving this vision still requires addressing challenges in various aspects such as technical integration, security, and user experience.