BitMart Research has released a detailed report on the innovative MCP+AI Agent framework, a new paradigm for artificial intelligence applications. The report delves into the advancements of the Model Context Protocol (MCP), its integration with crypto AI Agents, and its transformative impact on blockchain automation, decentralized applications, and cross-platform interoperability. The research findings highlight the framework’s potential to enhance AI capabilities, streamline complex integrations, and advance the future of AI within the blockchain ecosystem.
Introduction to the MCP Concept
The development of the Model Context Protocol (MCP) is aimed at addressing a core challenge in AI development, particularly the complexity of integrating external tools. The primary objective of MCP is to simplify the interactions of AI tools by standardizing communication protocols, thereby enabling seamless integration with a variety of external services. MCP fundamentally streamlines this process by establishing standardized interfaces and communication specifications, allowing AI models to interact with external tools more efficiently and effectively.
At its core, MCP establishes a unified communication standard for interactions between AI Agents and external tools, including blockchain data, smart contracts, and off-chain services. This standardization addresses the traditional development challenge of interface fragmentation, enabling AI Agents to seamlessly integrate with multi-chain data and tools while significantly enhancing their autonomous execution capabilities.
Integration of MCP and AI Agents
MCP and crypto AI Agents share a complementary relationship. AI Agents primarily focus on blockchain automation, smart contract execution, and crypto asset management, emphasizing privacy protection and integration with decentralized applications. Conversely, MCP prioritizes streamlining interactions between AI Agents and external systems through standardized protocols and context management, enhancing cross-platform interoperability and flexibility. By leveraging MCP protocols, crypto AI Agents can achieve more efficient cross-platform integration and operation, enhancing their execution capabilities.
For example, an AI Agent focused on DeFi can utilize MCP to access real-time market data and automatically optimize portfolios. Furthermore, MCP unlocks new collaborative possibilities: through MCP, multiple AI Agents can collaborate through functional specialization, combining diverse capabilities to accomplish complex tasks such as on-chain data analysis, market prediction, and risk management, thereby improving overall efficiency and reliability. For on-chain transaction automation, MCP coordinates various transaction and risk control agents to address issues such as slippage, transaction friction, and MEV (Miner Extractable Value), enabling safer and more efficient on-chain asset management.
Specifically, MCP enables AI Agents to utilize external data and services more reliably by defining clear interaction specifications. This avoids errors caused by inconsistent interfaces and ensures that AI Agents can consistently access the information they need. Additionally, MCP enables more advanced scenarios, such as collaboration between AI Agents, creating intelligent systems capable of handling complex financial tasks.
In the context of DeFi, MCP can greatly enhance trading efficiency. AI Agents can use MCP to access real-time market data and automatically execute trades, optimizing portfolios and reducing human error. Furthermore, MCP can be used to automate risk management, helping protect investors from losses by monitoring market conditions and adjusting portfolios accordingly.
In the broader blockchain ecosystem, MCP can facilitate cross-chain interoperability. AI Agents can use MCP to access data and services from different blockchains, creating decentralized applications that can operate across multiple platforms. This opens the door to new and innovative applications, such as cross-chain trading and decentralized lending.
However, the potential of MCP extends far beyond finance. It can also be used for various other applications, such as supply chain management, healthcare, and the Internet of Things. By providing a way to securely and reliably share data between AI Agents and external systems, MCP can help businesses automate processes, improve efficiency, and make better decisions.
A significant advantage of MCP is its flexibility. The protocol can adapt to various data formats and communication protocols, making it easy to integrate into existing systems. Additionally, MCP is decentralized, meaning it is not controlled by any single entity. This helps ensure fairness and transparency and reduces the risk of censorship.
While MCP is still in the early stages of development, it has the potential to revolutionize the landscape of AI applications. By providing a way to securely and reliably share data between AI Agents and external systems, MCP can help businesses automate processes, improve efficiency, and make better decisions. As AI technology continues to evolve, MCP is likely to play an increasingly important role in driving the future of AI.
For example, in supply chain management, MCP can be used to track goods throughout the entire process, from production to delivery. AI Agents can use MCP to access information about inventory levels, shipping times, and weather conditions, optimizing logistics and reducing delays. In healthcare, MCP can be used to securely share patient data, enabling doctors to make more informed decisions and provide more personalized care. In the Internet of Things, MCP can be used to connect various devices and collect data, enabling automation and improving efficiency.
A key feature of MCP is its modular design. This makes it easy for developers to build custom applications and extend the functionality of the protocol. Additionally, MCP supports various programming languages and platforms, making it easy to integrate into existing systems.
Another significant advantage of MCP is its security. The protocol uses encryption to protect data from unauthorized access and ensures that communication between AI Agents is secure. Furthermore, MCP is decentralized, meaning it is not controlled by any single entity. This helps ensure fairness and transparency and reduces the risk of censorship.
Related Projects
Several projects are exploring the potential of MCP. These projects are building applications based on MCP and contributing to the development of the protocol. Here are some notable projects:
DeMCP
DeMCP is a decentralized MCP network. It aims to provide autonomously developed open-source MCP services for AI Agents, offering developers a business revenue-sharing deployment platform for MCP and supporting one-stop access to mainstream Large Language Models (LLMs). Developers can acquire services through stablecoin payments (USDT, USDC). As of May 8, its token DMCP has a market capitalization of approximately $1.62 million.
DeMCP’s goal is to create a more open and accessible AI ecosystem. By providing free and open-source MCP services, DeMCP is lowering the barriers to entry for AI development and enabling more developers to build innovative AI-based applications. Furthermore, DeMCP’s business revenue-sharing model incentivizes developers to contribute to the platform and build high-quality MCP services.
At the heart of DeMCP is its decentralized MCP network. This network consists of nodes running MCP software, which collectively provide MCP services to AI Agents. The network is decentralized, meaning it is not controlled by any single entity. This helps ensure fairness and transparency and reduces the risk of censorship.
DeMCP also provides a business revenue-sharing deployment platform. This platform allows developers to deploy and sell their MCP services and share revenue with DeMCP. The platform provides developers with a way to monetize their work and incentivizes them to build high-quality services for the platform.
In addition, DeMCP supports one-stop access to mainstream Large Language Models (LLMs). This makes it easy for developers to integrate LLMs into their applications and leverage the power of LLMs.
DeMCP is working to create a more open and accessible AI ecosystem. By providing free and open-source MCP services, DeMCP is lowering the barriers to entry for AI development and enabling more developers to build innovative AI-based applications.
DARK
DARK is an MCP network running in a Trusted Execution Environment (TEE), built on the Solana blockchain. Its token $DARK is listed on Binance Alpha, with a market capitalization of approximately $118.1 million as of May 8. Currently, DARK’s first application is under development, aiming to provide efficient tool integration capabilities for AI Agents through TEE and MCP protocols, enabling developers to quickly connect to various tools and external services with simple configurations. While the product is not yet fully launched, users can join the early access phase via an email waitlist to participate in testing and provide feedback.
DARK focuses on providing secure and reliable MCP services. By running the MCP network in a TEE, DARK ensures that data and communication between AI Agents is secure. Additionally, DARK leverages the fast and low-cost transactions of the Solana blockchain to provide efficient MCP services.
DARK’s first application is designed to provide efficient tool integration capabilities for AI Agents. The application will enable developers to quickly connect to various tools and external services with simple configurations. This will reduce the complexity of AI development and enable more developers to build innovative AI-based applications.
DARK is working to create a more secure and efficient AI ecosystem. By providing an MCP network running in a TEE, DARK ensures that data and communication between AI Agents is secure. Additionally, DARK leverages the fast and low-cost transactions of the Solana blockchain to provide efficient MCP services.
Cookie.fun
Cookie.fun is a platform dedicated to AI Agents within the Web3 ecosystem, aiming to provide users with a comprehensive AI Agent index and analytics toolkit. The platform helps users understand and evaluate the performance of various AI Agents by showcasing metrics such as cognitive influence, adaptive intelligence capabilities, user engagement, and on-chain data. On April 24, the Cookie.API 1.0 update introduced a dedicated MCP server featuring plug-and-play agent-specific infrastructure designed for both developers and non-technical users, requiring no configuration.
Cookie.fun focuses on providing in-depth insights into AI Agents. By providing a comprehensive index and analytics toolkit, Cookie.fun helps users understand and evaluate the performance of various AI Agents. This allows users to make more informed decisions and select the AI Agents that best suit their needs.