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

1. Introduction to MCP Concept

In the field of artificial intelligence, traditional chatbots often rely on general dialogue models, lacking personalized settings, which leads to uniform responses and a lack of human touch. To address this issue, developers have introduced the concept of "character setting," endowing AI with specific roles, personalities, and tones, making its responses closer to user expectations. However, even if AI possesses rich "character settings," it remains a passive responder, unable to proactively execute tasks or perform complex operations.

To address 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 operational instructions based on preset rules and tools, automatically executing tasks and returning results. This transforms AI from a passive conversationalist into an active task executor.

Although Auto-GPT has achieved a certain degree of autonomy for AI, it still faces issues such as non-unified tool invocation 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 allows AI to easily invoke various external services, significantly simplifying the development process and improving efficiency.

2. The Integration of MCP and AI Agent

MCP and AI Agent complement each other. The AI Agent 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 the AI Agent and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility.

MCP provides 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 resolves the issue 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-type AI Agents can access market data in real time and automatically optimize their investment portfolios through MCP.

In addition, MCP has opened up a new direction for AI Agents: collaboration among multiple AI Agents. Through MCP, AI Agents can be divided by function to collectively 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 costs, and MEV during trading, achieving safer and more efficient on-chain asset management.

3. Related Projects

1. DeMCP

DeMCP is a decentralized MCP network that provides self-developed open-source MCP services for AI Agents, offers a deployment platform for developers with revenue-sharing opportunities, and enables one-stop access to mainstream large language models (LLM). Developers can obtain services through supported stablecoins.

2. DARK

DARK is a trusted execution environment based on Solana, under the MCP network of TEE(. Its first application is currently under development and will provide efficient tool integration capabilities for AI Agents through the TEE and MCP protocols, allowing developers to quickly access various tools and external services through simple configuration.

) 3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, providing comprehensive AI Agent indexes and analytical tools. The platform showcases metrics such as the cognitive influence, smart following ability, user interaction, and on-chain data of AI Agents, helping users evaluate the performance of different AI Agents. Recent updates have introduced dedicated MCP servers, including plug-and-play MCP servers for agents, 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, aimed at constructing blockchain-native AI infrastructure by extending MCP. The platform offers a scalable and interoperable data protocol for Web3-based AI applications, and plans 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 the BNB Chain and Solana, with over 10 billion rows of data, and will also support MCP data servers on the Ethereum mainnet and Base chain in the future.

4. Future Development

The MCP protocol, as a new narrative for the integration of AI and blockchain, demonstrates tremendous potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, particularly in decentralized finance scenarios where it has wide application prospects. However, most MCP-based projects are still in the proof-of-concept stage and have not yet launched mature products, leading to a crisis of market trust.

Accelerating product development, ensuring a close connection between the token and the actual product, and enhancing user experience are the core issues facing the current MCP project. In addition, the promotion of the MCP protocol within the cryptocurrency ecosystem still faces challenges in technical integration, requiring significant development resources to unify the smart contract logic and data structures among different blockchains and DApps.

Despite the challenges, the MCP protocol itself still demonstrates significant market development potential. With advancements in AI technology and the 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. 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.

As an important auxiliary force for the integration of AI and blockchain, the MCP protocol is expected to become a key engine driving the next generation of AI Agents as technology matures and application scenarios expand. However, realizing this vision still requires addressing various challenges such as technical integration, security, and user experience.

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