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

1. Introduction to MCP Concept

Traditional chatbots often lack personalized character settings, resulting in responses that are monotonous and lack warmth. To address this issue, developers have introduced the concept of "character setting," assigning specific roles, personalities, and tones to AI. However, even with rich "character settings," AI is still just a passive responder, unable to proactively execute tasks or perform complex operations.

In order for AI to actively perform tasks, the Auto-GPT project has emerged. It allows developers to define a series of tools and functions for AI and register these tools into the system. When a user makes a request, 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 challenges such as inconsistent tool calling formats and poor cross-platform compatibility. To address these challenges, 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.

The MCP protocol significantly simplifies the interaction process between AI models and external tools by defining standardized interfaces and communication protocols, allowing AI models to execute complex tasks more quickly and efficiently.

2. The Integration of MCP and AI Agent

MCP and AI Agent have a complementary relationship. AI Agent mainly focuses on the automation of blockchain operations, execution of smart contracts, and management of crypto assets, emphasizing privacy protection and the integration of decentralized applications. On the other hand, MCP places more emphasis on simplifying the interaction between AI Agent and external systems, providing standardized protocols and context management, thereby enhancing 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 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 AI Agents can obtain market data in real time and automatically optimize portfolios through MCP. In addition, MCP opens up a new direction for AI Agents, specifically collaboration among multiple AI Agents: through MCP, AI Agents can collaborate based on functional divisions to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby enhancing overall efficiency and reliability.

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 for MCP developers to share commercial profits, and achieving one-stop access to mainstream large language models (LLM). Developers can access services by supporting stablecoins.

2. DARK

DARK is an MCP network built on Solana, operating in a trusted execution environment ( TEE ). Its first application is currently in development, which will provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols, allowing developers to quickly access various tools and external services through simple configurations.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents within the Web3 ecosystem, aimed at 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 the mental influence of AI Agents, smart following capabilities, user interactions, and on-chain data. Recently, Cookie.fun launched exclusive MCP servers, which include plug-and-play dedicated MCP servers for AI Agents, designed for both 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 the MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, with plans to simplify the development process by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby promoting the practical application of AI in a blockchain environment.

4. Future Development

The MCP protocol, as a new narrative of the integration of AI and blockchain, demonstrates 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 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 phenomenon reflects a crisis of trust in the MCP project by the market, primarily stemming from the long product development cycle and the lack of practical application implementation. Therefore, how to accelerate the product development progress, ensure a close connection between the token and the actual product, 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 standardized MCP server will still require a significant amount of development resources.

Despite facing the aforementioned challenges, the MCP protocol itself still demonstrates tremendous market development potential. With the continuous advancement of AI technology and the gradual maturity of the MCP protocol, it is expected to achieve broader applications in areas such as DeFi and DAO in the future. For example, AI agents can obtain on-chain data in real-time through the MCP protocol, 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.

The MCP protocol, as an important auxiliary force in the integration of AI and blockchain, is expected to become a key engine driving the next generation of AI Agents with the continuous maturity of technology and the expansion of application scenarios. However, achieving this vision still requires addressing challenges in technology integration, security, user experience, and other areas.