MCP+AI Agent: A new framework for artificial intelligence applications

1. Introduction to the concept of MCP

Previously, in the field of artificial intelligence, traditional chatbots mostly relied on general conversation models and lacked personalized role settings, which often made their responses appear single and lack human touch. To solve this problem, developers have introduced the concept of "personality", which is to give AI specific roles, personality, and tone to make it respond more closely to users' expectations. However, even if AI has a rich "personality", it is still a passive responder and cannot actively perform tasks or perform complex operations. Therefore, the open source project Auto-GPT came into being. Auto-GPT 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 will generate corresponding operation instructions based on preset rules and tools, automatically execute tasks and return results. This method has transformed AI from a passive interlocutor to an active task AI.

Although Auto-GPT has realized the autonomous execution of AI to a certain extent, it still faces problems such as inconsistent tool call formats and poor cross-platform compatibility. To solve these problems, MCP (Model Context Protocol) came into being, aiming to solve the major challenges facing AI during development, especially the complexity when integrating with external tools. The core goal of MCP is to simplify the interaction between AI and external tools, and by providing unified communication standards, it enables AI to easily call various external services. Traditionally, to have large-scale models perform complex tasks (such as querying weather or visiting web pages), developers need to write a lot of code and tool descriptions, which greatly increases the difficulty and time cost of development. 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 efficiently.

2. The integration of MCP and AI Agent

MCP and cryptographic AI Agent are in a complementary relationship. The difference between the two is that AI Agent mainly focuses on the automated operation of blockchain, smart contract execution and cryptographic asset management, emphasizing privacy protection and the integration of decentralized applications. MCP focuses more on simplifying the interaction between AI Agents and external systems, providing standardized protocols and context management, and enhancing cross-platform interoperability and flexibility. Encrypted AI Agents can be more efficiently integrated and operated across the MCP protocol, thereby enhancing their execution capabilities.

Previous AI Agents had certain execution capabilities, such as executing transactions through smart contracts, managing wallets, etc. However, these functions are usually predefined and lack flexibility and adaptability. The core value of MCP lies in providing a unified communication standard for the interaction between AI Agent and external tools (including blockchain data, smart contracts, off-chain services, etc.). This standardization solves the problem of interface fragmentation in traditional development, allowing AI Agent to seamlessly connect multi-chain data and tools, and greatly enhances the autonomous execution capabilities of AI Agent. For example, DeFi AI Agent can obtain market data in real time and automatically optimize investment portfolios through MCP. In addition, MCP has opened a new direction for AI Agent, namely, multiple AI Agents collaborate: Through MCP, AI Agent can work according to the division of labor and cooperation, combining and completing complex tasks such as on-chain data analysis, market forecasting, risk control management, etc., improving overall efficiency and reliability. On-chain transaction automation: MCP connects various transactions and risk control agents to solve problems such as slippage, transaction wear, and MEV in transactions, and achieve safer and more efficient on-chain asset management.

3. Related projects

1.DeMCP

DeMCP is a decentralized MCP network. It is committed to providing AI Agent with self-developed and open source MCP services, providing MCP developers with a deployment platform that shares commercial benefits, and realizing one-stop access to mainstream large language models (LLMs). Developers can obtain services by supporting stablecoins (USDT, USDC). As of May 8, its token DMCP had a market value of approximately $1.62M.

2.DARK

DARK is an MCP network under a Trusted Execution Environment (TEE), built on Solana. Token $DARK is launched on Binance Alpha, with a market value of approximately US$11.81 million as of May 8. Currently, DARK's first application is in development. It will provide AI Agent with efficient tool integration capabilities through TEE and MCP protocols, allowing developers to quickly access multiple tools and external services through simple configurations. Although the product has not been fully released yet, users can join the early experience stage through email candidates, participate in the test and provide feedback.

3.Cookie.fun

Cookie.fun is a platform focused on AIAgent in the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analysis tool. ​The platform helps users understand and evaluate the performance of different AI Agents by demonstrating indicators such as the mental influence, intelligent following capabilities, user interaction and on-chain data. On April 24, the Cookie.API1.0 update launched a dedicated MCP server, which includes a plug-and-play agent-specific MCP server, designed for developers and non-technical personnel without any configuration.

 Source: [X](https://x.com/cookiedotfun/status/1915422210589667444)

4.SkyAI

SkyAI is a Web3 data infrastructure project built on BNB Chain, aiming to build a blockchain-native AI infrastructure by extending MCP. The platform provides scalable and interoperable data protocols for Web3-based AI applications, and plans to simplify the development process by integrating multi-chain data access, AI proxy deployment and protocol-level utilities, thereby promoting the practical application of AI in the blockchain environment. At present, SkyAI supports aggregated data sets from BNB Chain and Solana, with data volume exceeding 10 billion rows. In the future, MCP data servers that support Ethereum main network and Base chain will be launched. Its token SkyAI was launched on Binance Alpha, with a market value of approximately US$42.7 million as of May 8.

4. Future development

As a new narrative of the integration of AI and blockchain, the MCP protocol has shown great potential in improving data interaction efficiency, reducing development costs, enhancing security and privacy protection, especially in decentralized finance and other scenarios, with a wide range of application prospects. However, most MCP-based projects are still in the proof of concept stage and have not yet launched mature products, resulting in the continued decline in the price of their tokens after they went online. For example, the price of the DeMCP token has fallen by 74% less than a month after it went online. ​This phenomenon reflects the market's trust crisis in MCP projects, which mainly stems from the long product development cycle and the lack of practical application implementation.​ Therefore, how to speed up product development progress, ensure the close connection between tokens and actual products, and improve user experience will be the core issues facing current MCP projects. ​In addition, the promotion of MCP protocol in the encryption ecosystem still faces the challenges of technological integration. ​Due to the differences in the logic and data structure of smart contracts between different blockchains and DApps, a unified and standardized MCP server still needs to invest a lot of development resources.​

Despite the above challenges, the MCP protocol itself still shows huge market development potential. ​With the continuous advancement of AI technology and the gradual maturity of MCP protocols, it is expected to achieve wider applications in the fields of DeFi, DAO and other fields in the future. For example, AI agents can obtain on-chain data in real time through the MCP protocol, execute automated transactions, and improve the efficiency and accuracy of market analysis. ​In addition, the decentralized characteristics of the MCP protocol are expected to provide a transparent and traceable operating platform for AI models, promoting the decentralization and assetization process of AI assets. ​MCP protocol is an important auxiliary force for the integration of AI and blockchain. With the continuous maturity of technology and the expansion of application scenarios, the MCP protocol is expected to become an important engine to promote the next generation of AI Agents. However, to realize this vision, it still needs to solve many challenges in technology integration, security, user experience, and other aspects.