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

Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often lack personalized character settings, resulting in monotonous responses that lack warmth. To solve this problem, developers have introduced the concept of "character setting," giving AI specific roles, personalities, and tones. However, even with rich "character settings," AI remains merely a passive responder, unable to actively perform tasks or engage in complex operations.

In order to transform AI from a passive conversationalist into an active task executor, the open-source project Auto-GPT has emerged. It allows developers to define a series of tools and functions for the AI and register these tools within the system. When users make requests, Auto-GPT generates corresponding operation instructions based on preset rules and tools, automatically executes tasks, and returns results.

Although Auto-GPT has achieved a certain degree of autonomous execution of AI, it still faces issues such as the lack of a unified tool calling format and poor cross-platform compatibility. To address these problems, MCP (Model Context Protocol) has emerged. MCP aims to simplify the interaction between AI and external tools by providing a unified communication standard, enabling AI to easily call various external services. This standardized interface and communication specification significantly simplify the development process, allowing AI models to interact with external tools more quickly and effectively.

The Synergistic Effect of MCP and AI Agent

MCP and the crypto AI Agent complement each other. The AI Agent primarily focuses on automated operations in blockchain, smart contract execution, and management of crypto assets, emphasizing privacy protection and integration of decentralized applications. MCP, on the other hand, focuses on simplifying the interaction between the AI Agent and external systems, providing standardized protocols and context management, enhancing interoperability and flexibility across platforms.

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 problem 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 access market data in real-time and automatically optimize their investment portfolios.

In addition, MCP has opened up a new direction for AI Agent collaboration, where multiple AI Agents can work together. Through MCP, AI Agents can collaborate by dividing responsibilities and combine to complete complex tasks such as on-chain data analysis, market prediction, and risk management, enhancing overall efficiency and reliability. In terms of on-chain trading automation, MCP connects various trading and risk control Agents, addressing issues such as slippage, trading friction, and MEV, achieving safer and more efficient on-chain asset management.

Related Project Overview

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 that shares commercial revenue, and achieving one-stop access to mainstream large language models (LLM). Developers can access services through supported stablecoins.

DARK

DARK is an MCP network built on Solana under a trusted execution environment ( TEE ). Its first application is currently in 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 with simple configurations.

Cookie.fun

Cookie.fun is a platform focused on AIAgents within 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 metrics such as AI Agent's mental influence, intelligent following ability, user interaction, and on-chain data. Recently, the Cookie.API 1.0 update launched a dedicated MCP server, which includes plug-and-play smart agent-specific MCP servers designed for developers and non-technical personnel, requiring no configuration.

SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to construct blockchain-native AI infrastructure by extending 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 blockchain environments 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 the data volume exceeding 10 billion rows. In the future, MCP data servers supporting the Ethereum mainnet and Base chain will also be launched.

Future Development Prospects and Challenges

The MCP protocol, as a new narrative of the fusion of AI and blockchain, has shown great potential in improving data interaction efficiency, reducing development costs, enhancing security, and protecting privacy, especially in decentralized finance scenarios where it has broad application prospects. However, most current MCP-based projects are still in the proof-of-concept stage and have not launched mature products, leading to a continuous decline in their token prices after going online. This reflects a crisis of trust in the market towards MCP projects, mainly stemming from the long product development cycle and 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 use the MCP protocol to obtain on-chain data in real-time, 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 of AI assets.

However, achieving this vision still requires addressing challenges in multiple areas such as technical integration, security, and user experience. How to accelerate product development, ensure a close connection between tokens and actual products, and enhance user experience will be the core issues facing the current MCP project. In addition, due to the 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 for the integration of AI and blockchain, is expected to become a key engine driving the next generation of AI Agents with the continuous maturation of technology and the expansion of application scenarios. However, to fully realize its potential, the industry still needs to work together to address the numerous challenges currently faced.