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

1. Introduction to MCP Concept

Traditional chatbots in the field of artificial intelligence often lack personalized character settings, resulting in responses that are monotonous and lack human warmth. To address this issue, developers have introduced the concept of "personality setting" to endow AI with specific roles, personalities, and tones. However, even with a rich "personality setting," AI remains a passive responder and cannot actively execute tasks or perform complex operations.

The Auto-GPT project has emerged, allowing developers to define tools and functions for AI and register them in the system. When users make a request, 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.

Despite Auto-GPT achieving autonomous execution of AI, it still faces issues such as inconsistent tool calling formats and poor cross-platform compatibility. MCP (Model Context Protocol) has emerged to address the main challenges in the AI development process, particularly the complexity when integrating with external tools. The core objective of MCP is to simplify the interaction between AI and external tools by providing a unified communication standard, enabling AI to easily call various external services.

Traditionally, executing complex tasks with large-scale models requires a significant amount of code and tool instructions, greatly increasing development difficulty and time costs. The MCP protocol simplifies this process significantly by defining standardized interfaces and communication specifications, allowing AI models to interact with external tools more quickly and efficiently.

2. The Integration of MCP and AI Agent

MCP and AI Agent have a complementary relationship. The AI Agent primarily 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 to enhance cross-platform interoperability and flexibility.

Traditional AI Agents have 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 addresses the issue of interface fragmentation in traditional development, enabling AI Agents to seamlessly integrate 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 portfolios through MCP. In addition, MCP opens up new directions for AI Agents to collaborate, allowing multiple AI Agents to work together based on functional division: through MCP, AI Agents can collaborate to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, 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, transaction costs, and MEV during trading, achieving safer and more efficient on-chain asset management.

Three, Related Projects

1. 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 revenue with MCP developers, and achieving one-stop access to mainstream large language models (LLM). Developers can obtain services through stablecoin support.

2. DARK

DARK is an MCP network built on Solana under the trusted execution environment ( TEE ). Its first application is in the development stage and will 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. Currently, users can join the early experience phase through an email waitlist, participate in testing, and provide feedback.

3. 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. On April 24th, the Cookie.API 1.0 update launched a dedicated MCP server, which includes plug-and-play MCP servers specifically for agents, designed for developers and non-technical users with no configuration required.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aimed at constructing a blockchain-native AI infrastructure by expanding 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 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 plans to launch MCP data servers supporting 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 immense potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, especially in decentralized finance scenarios with broad application prospects. However, most MCP-based projects are currently still in the proof-of-concept stage and have not launched mature products, leading to a continuous decline in their token prices after launch. This reflects a trust crisis in the market regarding MCP projects, primarily stemming from the lengthy product development cycle and a lack of practical applications.

Therefore, how to accelerate product development progress, ensure a close connection between tokens and actual products, 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 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 tremendous potential for market development. As AI technology continues to advance and the MCP protocol matures, 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 trades, and improve market analysis efficiency and accuracy. 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 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 for 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.