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

Introduction to the Concept of MC

In the field of artificial intelligence, traditional chatbots often rely on general dialogue models, lacking personalized settings, resulting in responses that are often monotonous and dull. To address this issue, developers have introduced the concept of "character setting," endowing AI with specific roles, personalities, and tones, making its responses more aligned with user expectations. However, even with rich "character settings," AI remains a passive responder, unable to actively perform tasks or engage in complex operations.

To overcome this limitation, the open-source project Auto-GPT was born. It allows developers to define a series of tools and functions for AI and register them in the system. When users make a request, Auto-GPT generates corresponding operation instructions based on preset rules and tools, automatically executes tasks, and returns results. This transforms AI from a passive conversationalist to 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, the MCP (Model Context Protocol) 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. Traditionally, to enable large-scale models to perform complex tasks, developers had to write a large amount of code and tool documentation, greatly increasing development difficulty and time costs. The MCP protocol significantly simplifies this process by defining standardized interfaces and communication specifications, allowing AI models to interact with external tools more quickly and effectively.

The Integration 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, focuses on simplifying the interaction between AI Agent and external systems, providing standardized protocols and context management to enhance cross-platform interoperability and flexibility.

Traditional AI Agents possess certain execution capabilities, such as executing transactions through smart contracts and managing wallets, but these functions are usually predefined, lacking flexibility and adaptability. 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 solves the problem of interface fragmentation in traditional development, allowing AI Agents to seamlessly integrate with multi-chain data and tools, significantly enhancing their autonomous execution capabilities.

For example, DeFi-type AI Agents can access market data in real time and automatically optimize portfolios through MCP. In addition, MCP opens up new directions for AI Agents, allowing multiple AI Agents to collaborate: through MCP, AI Agents can collaborate based on functional division of labor to 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 to address issues such as slippage, transaction friction, and MEV in trading, 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 developers a profit-sharing deployment platform for commercial gain, enabling one-stop access to mainstream large language models (LLM). Developers can obtain services by supporting stablecoins. As of May 8, its token DMCP has a market capitalization of approximately 1.62 million dollars.

DARK

DARK is an MCP network built on a trusted execution environment (TEE) based on Solana. Its first application is under development and will provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol, 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 to participate in testing and provide feedback.

Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing users with comprehensive AI Agent indices and analytical tools. The platform displays metrics such as the cognitive influence of AI Agents, intelligent following capabilities, user interactions, and on-chain data, helping users understand and evaluate the performance of different AI Agents. On April 24, the Cookie.API 1.0 update introduced dedicated MCP servers, which include plug-and-play MCP servers specifically for agents, designed for both 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 through the expansion of MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, planning to simplify the development process and promote the practical application of AI in a blockchain environment by integrating multi-chain data access, AI agent deployment, and protocol-level utilities. Currently, SkyAI supports aggregated datasets from the BNB Chain and Solana, with data volume exceeding 10 billion rows, and will soon launch MCP data servers supporting Ethereum mainnet and Base chain.

Future Development

The MCP protocol, as a new narrative of the integration of AI and blockchain, has shown great potential in enhancing data interaction efficiency, reducing development costs, and improving security and privacy protection, especially in decentralized finance scenarios where it has broad application prospects. However, most projects based on MCP are still in the proof-of-concept phase and have not yet launched mature products, resulting in continuous declines in their token prices after going live. This reflects a crisis of trust in MCP projects in the market, mainly due to the long product development cycles and the lack of practical applications.

Therefore, accelerating the product development process, ensuring a close connection between tokens and actual products, and enhancing user experience will be the core issues faced by the current MCP project. In addition, the promotion of the MCP protocol within the crypto ecosystem still faces challenges in technical integration. Due to differences in smart contract logic and data structures between different blockchains and DApps, a standardized MCP server will still require a significant amount of development resources.

Despite facing challenges, the MCP protocol itself still shows great 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 fields 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 trades, and enhance the efficiency and accuracy of market analysis. In addition, 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.

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 as technology matures and application scenarios expand. However, to realize this vision, challenges in technology integration, security, user experience, and other aspects still need to be addressed.