Concept of MC and its Application in AI Agents

Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often rely on general dialogue models, lacking personalized character settings, leading to uniform responses that lack warmth. 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 proactively execute tasks or perform complex operations.

To solve this problem, the open-source project Auto-GPT was born. It allows developers to define a series of tools and functions for AI and register these tools into the system. When users make requests, Auto-GPT generates operational instructions based on preset rules and tools, automatically executes tasks, and returns results, transforming AI from a passive responder into an active task executor.

Despite the fact that Auto-GPT has achieved a certain degree of autonomous execution by AI, it still faces issues such as inconsistent tool calling formats 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, allowing AI to easily call various external services. Traditionally, making large-scale models perform complex tasks requires writing a lot of code and tool descriptions, greatly increasing development difficulty and time costs. The MCP protocol significantly simplifies this process by defining standardized interfaces and communication specifications, enabling AI models to interact with external tools more quickly and effectively.

The Integration of MCP and AI Agent

MCP and AI Agent have a complementary relationship. AI Agent mainly focuses on automation operations in blockchain, execution of smart contracts, and management of crypto assets, emphasizing privacy protection and integration of decentralized applications. 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.

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 interface fragmentation in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing autonomous execution capabilities. For example, DeFi-type AI Agents can access market data in real-time and automatically optimize their investment portfolios through MCP.

In addition, MCP opens up a new direction for AI Agents, which is the collaboration of multiple AI Agents. Through MCP, AI Agents can collaborate based on functional divisions, combining to complete complex tasks such as on-chain data analysis, market prediction, and risk management, thereby improving 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 in 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 that shares commercial profits with MCP developers, and achieving one-stop access to mainstream large language models (LLM). Developers can access services by supporting stablecoins.

DARK

DARK is an MCP network built on Solana under a Trusted Execution Environment (TEE). Its first application is in the development stage and will provide efficient tool integration capabilities for AI Agents through the TEE and MCP protocols, allowing developers to quickly access a variety of tools and external services with simple configuration.

Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, aiming to provide users with comprehensive AI Agent indices and analytical tools. The platform helps users understand and evaluate the performance of different AI Agents by showcasing metrics such as the cognitive influence of AI Agents, intelligent following capabilities, user interactions, and on-chain data. Recent updates introduced dedicated MCP servers, 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 a blockchain-native AI infrastructure by expanding the 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 over 10 billion rows of data, and will soon launch MCP data servers supporting the Ethereum mainnet and Base chain.

Future Development

The MCP protocol, as a new narrative of the fusion of AI and blockchain, shows great potential in improving data interaction efficiency, reducing development costs, and enhancing security and privacy protection, especially in scenarios such as decentralized finance. However, most current projects based on MCP 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 live. This reflects a trust crisis in the market for MCP projects, mainly stemming from the long product development cycle and the lack of practical application implementation.

How to accelerate product development, ensure a close connection between the token and the actual product, and enhance user experience will be the core issues faced by the current 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 standardized MCP server still requires a significant investment of development resources.

Despite facing the aforementioned challenges, the MCP protocol itself still demonstrates 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 trading, 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.

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, achieving this vision still requires addressing challenges in various areas such as technology integration, security, and user experience.