1. Introduction to MCP Concept

In the field of artificial intelligence, traditional chatbots have often relied on generic dialogue models, lacking personalized character settings, which resulted in responses that were often monotonous and lacked human touch. To address this issue, developers introduced the concept of "character setting," which assigns specific roles, personalities, and tones to AI, making its responses more aligned with user expectations. However, even with rich "character settings," AI remains a passive responder, unable to actively perform tasks or execute complex operations. This led to the emergence of the open-source project Auto-GPT. Auto-GPT allows developers to define a series of tools and functions for AI and register these tools within the system. When users make requests, Auto-GPT generates corresponding operational instructions based on preset rules and tools, automatically executing tasks and returning results. This approach transforms AI from a passive conversationalist into an active task-oriented AI.

Although Auto-GPT has achieved a degree of autonomous execution for AI, it still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility. To solve these problems, MCP (Model Context Protocol) was introduced, aiming to address the main challenges faced by AI during development, especially the complexity of integrating with external tools. The core goal 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, to enable large-scale models to perform complex tasks (such as querying weather or accessing web pages), developers needed to write a significant amount 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, allowing AI models to interact with external tools more quickly and effectively.

# 2. Integration of MCP and AI Agent

The relationship between MCP and encrypted AI Agents is complementary. The distinction between the two lies in that AI Agents primarily focus on automated operations in blockchain, smart contract execution, and management of encrypted assets, emphasizing privacy protection and integration of decentralized applications. MCP focuses more on simplifying the interaction between AI Agents and external systems, providing standardized protocols and context management, enhancing cross-platform interoperability and flexibility. Encrypted AI Agents can achieve more efficient cross-platform integration and operations through the MCP protocol, thereby enhancing their execution capabilities.

Previous AI Agents had certain execution capabilities, such as executing transactions through smart contracts and managing wallets. However, these functions were often 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 fragmented interfaces in traditional development, enabling AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing the autonomous execution capabilities of AI Agents. For example, DeFi-type AI Agents can use MCP to obtain market data in real-time and automatically optimize investment portfolios. Furthermore, MCP opens up a new direction for AI Agent collaboration: through MCP, AI Agents can collaborate based on functional division, combining to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, improving overall efficiency and reliability. On-chain transaction automation: MCP connects various trading and risk control Agents, addressing issues such as slippage, transaction wear, and MEV, achieving safer and more efficient on-chain asset management.

# 3. Related Projects

## 1. DeMCP

DeMCP is a decentralized MCP network. It aims to provide self-developed open-source MCP services for AI Agents, offering a deployment platform for MCP developers with revenue sharing, and achieving one-stop access to mainstream large language models (LLM). Developers can obtain services by supporting stablecoins (USDT, USDC). As of May 8, its token DMCP has a market value of approximately $1.62M.

## 2. DARK

DARK is an MCP network built on Solana under a trusted execution environment (TEE). The token $DARK was launched on Binance Alpha, with a market value of approximately $11.81 million as of May 8. Currently, DARK's first application is in the development stage, which will provide efficient tool integration capabilities for AI Agents through TEE and the MCP protocol, allowing developers to quickly access various tools and external services with simple configurations. Although the product has not been fully released, users can join the early experience phase through an email waitlist to participate in testing and provide feedback.

3. Cookie.fun

Cookie.fun is a platform focused on AI Agents in the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analysis tools. The platform helps users understand and evaluate the performance of different AI Agents by showcasing metrics such as their mental influence, intelligent following ability, user interaction, and on-chain data. On April 24, the Cookie.API 1.0 update launched a dedicated MCP server, which includes plug-and-play agent-specific MCP servers designed for developers and non-technical users, requiring no configuration.

Information source: X

## 4. 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 scalable and interoperable data protocols for Web3-based AI applications, planning 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 blockchain environments. 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. Its token SkyAI was launched on Binance Alpha, with a market value of approximately $42.7 million as of May 8.

# 4. Future Development

The MCP protocol, as a new narrative for the integration of AI and blockchain, demonstrates significant potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, especially in scenarios like decentralized finance, with 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 launch; for example, the price of the DeMCP token has dropped by 74% within less than a month of its launch. This phenomenon reflects a crisis of trust in the market regarding MCP projects, primarily stemming from lengthy product development cycles and a lack of practical applications. Therefore, how to accelerate product development, ensure a close correlation between tokens and actual products, and enhance user experience will be the core issues facing current MCP projects. Additionally, 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 significant development resources.

Despite these challenges, the MCP protocol itself still shows great 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 fields such as DeFi and DAO in the future. For example, AI agents can use the MCP protocol to obtain on-chain data in real-time, execute automated trading, and improve the efficiency and accuracy of market analysis. Furthermore, the decentralized nature of the MCP protocol is expected to provide AI models with a transparent and traceable operating platform, 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, achieving this vision still requires addressing challenges in technical integration, security, and user experience.

Risk Warning:

The information provided is for reference only and should not be considered as advice to buy, sell, or hold any financial assets. All information is provided in good faith. However, we make no express or implied representations or warranties regarding the accuracy, adequacy, effectiveness, reliability, availability, or completeness of such information.

All cryptocurrency investments (including returns) are inherently highly speculative and involve significant risk of loss. Past, hypothetical, or simulated performance does not necessarily represent future results. The value of digital currencies may rise or fall, and there may be significant risks associated with buying, selling, holding, or trading digital currencies. You should carefully consider whether trading or holding digital currencies is suitable for you based on your individual investment objectives, financial situation, and risk tolerance. BitMart does not provide any investment, legal, or tax advice.