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

1. MCP Concept Analysis

In the field of artificial intelligence, traditional chatbots often lack personalization and proactivity. To address this issue, developers have introduced the concept of "character setting," giving AI specific roles and personalities. However, even with a rich "character setting," AI still remains a passive responder and cannot proactively perform complex tasks.

To break through this limitation, the open-source project Auto-GPT has emerged. It allows developers to define tools and functions for AI, enabling the AI to automatically execute tasks based on predefined rules. Although Auto-GPT has achieved a certain degree of autonomous execution, it still faces issues such as inconsistent tool invocation formats and poor cross-platform compatibility.

MCP (Model Context Protocol) was born to address the complexities of integrating AI with external tools. MCP simplifies the interaction process between AI and external services by providing a unified communication standard. This standardized interface greatly reduces development difficulty and time costs, allowing AI models to interact more efficiently with external tools.

2. Collaboration between MCP and AI Agent

MCP and AI Agent form a complementary relationship. The AI Agent primarily focuses on blockchain operations, smart contract execution, and cryptocurrency asset management, emphasizing privacy protection and decentralized application integration. In contrast, MCP 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.

The core value of MCP lies in providing a unified standard for the interaction between AI Agents and external tools (such as blockchain data, smart contracts, off-chain services, etc.). This standardization addresses the issue of fragmented interfaces in traditional development, allowing AI Agents to seamlessly connect with multi-chain data and tools, significantly enhancing their autonomous execution capabilities. For example, DeFi-type AI Agents can obtain real-time market data through MCP and automatically optimize their investment portfolios.

In addition, MCP has opened up a new direction for AI Agents - multi-Agent collaboration. Through MCP, AI Agents with different functions can work together 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 can connect various trading and risk control Agents to address issues such as slippage, trading friction, and MEV in transactions, achieving safer and more efficient on-chain asset management.

3. Overview of Related Projects

1. DeMCP

DeMCP is a decentralized MCP network dedicated to providing self-developed open-source MCP services for AI Agents. It offers a deployment platform that shares commercial revenue with MCP developers, enabling one-stop access to mainstream large language models. Developers can obtain services through supporting stablecoins.

2. DARK

DARK is a trusted execution environment ( TEE ) based on Solana, under the MCP network. Its first application is currently under development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols. This will enable 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, providing users with comprehensive AI Agent indices and analytical tools. The platform showcases metrics such as the cognitive influence of AI Agents, intelligent following capabilities, user interactions, and on-chain data, helping users assess the performance of different AI Agents. Recently, the Cookie.API1.0 update launched a dedicated MCP server, offering plug-and-play MCP services specifically for developers and non-technical personnel.

4. SkyAI

SkyAI is a Web3 data infrastructure project built on the BNB Chain, aiming to create a blockchain-native AI infrastructure by extending the MCP. The platform provides a scalable and interoperable data protocol for Web3-based AI applications, simplifying the development process 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 further support MCP data services from the Ethereum mainnet and Base chain in the future.

4. Future Development Prospects

The MCP protocol, as a new narrative of the integration of AI and blockchain, demonstrates great potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection, especially in decentralized finance scenarios where it has broad application prospects. However, most of the current MCP-based projects are still in the proof-of-concept stage and have not yet launched mature products, leading to a continuous decline in their token prices after going live. This reflects a crisis of trust in the MCP projects in the market, primarily stemming from the long product development cycle and lack of practical application.

In the future, the development of the MCP protocol faces several key challenges:

1. Accelerate product development progress to ensure a close connection between the tokens and the actual products.
2. Enhance user experience and increase product attractiveness.
3. Overcome technical integration barriers to achieve standardized MCP services between different blockchains and DApps.

Despite facing challenges, the MCP protocol still demonstrates great market potential. With advancements in AI technology and the maturation 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 access on-chain data in real time through the MCP protocol to execute automated trades, enhancing market analysis efficiency and accuracy. Additionally, 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 in the integration of AI and blockchain, the MCP protocol is expected to become a key engine for driving the next generation of AI Agents. However, to realize this vision, challenges related to technical integration, security, and user experience still need to be addressed. As these issues are gradually resolved, the MCP protocol will play an increasingly important role in the future development of artificial intelligence applications.