Mahe, Seychelles, May 9, 2025 (Globe Newswire) – Bitmart Research, research unit at Bitmart Exchange, has released a detailed report on the innovative MCP+AI agent framework, a new paradigm for AI applications. This report explores advances in Model Context Protocol (MCP), integration with encrypted AI agents, and transformative impacts on blockchain automation, distributed applications, and cross-platform interoperability. The findings highlight the potential of this framework to enhance AI capabilities, streamline complex integration, and drive the future of AI in the blockchain ecosystem.
1. Introducing the concept of MCP
In the field of artificial intelligence, traditional chatbots have long relied on general dialogue models that lack personalized characterization, resulting in monotonous, impersonal responses. To address this limitation, developers introduced the concept of “persona.” More appropriate responses to AIs that award specific roles, personalities, and voice patterns. However, even with well-defined personas, these systems remained passive responders who were unable to actively perform tasks or handle complex operations. This has led to the open source project Auto-GPT. This allows developers to define a suite of tools and features in AI systems. By registering these tools within the framework, Auto-GPT can generate operational commands based on predefined rules and resources when processing user requests, performing tasks autonomously, or returning results. This advancement transforms AI from a passive conversation agent to a proactive, task-oriented system.
Despite progress in enabling autonomous AI operations in Auto-GPT, challenges persisted with inconsistent tool invocation formats and inadequate cross-platform compatibility. The Model Context Protocol (MCP) was developed to address these core challenges in AI development, particularly the complexity of integrating external tools. The main purpose of MCP is to streamline AI-Tool interactions via standardized communications protocols, allowing for seamless integration of diverse external services. Traditionally, implementing complex features such as weather queries and web access in large language models requires extensive custom coding and tool documentation. This is a process that greatly increases development complexity and time investment. MCP fundamentally simplifies this process by establishing standardized interfaces and communication specifications, allowing AI models to interact more efficiently and effectively with external tools.
2. MCP and AI Agent Integration
MCP and encrypted AI agents share complementary relationships, with important distinctions in their focus. AI agents focus primarily on blockchain automation, smart contract execution, cryptocurrency management, emphasizing privacy protection and integration with decentralized applications. Conversely, MCP prioritizes simplifying interactions between AI agents and external systems via standardized protocols and context management, increasing cross-platform interoperability and flexibility. By leveraging the MCP protocol, encrypted AI agents can achieve more efficient cross-platform integration and operation, which improves execution capabilities.
Previous generation AI agents had basic operational capabilities, such as running transactions through smart contracts and managing wallets. However, these features were usually predefined and lacked flexibility and adaptability. The core value of MCP is to establish unified communications standards for interaction between AI agents and external tools, such as blockchain data, smart contracts, and off-chain services. This standardization addresses the challenges of traditional development of interface fragmentation, allowing AI agents to seamlessly integrate with multi-chain data and tools, while significantly enhancing autonomous execution capabilities. For example, Defi-focused AI agents using MCPs can access real-time market data and automatically optimize their investment portfolio. Furthermore, MCP unlocks new possibilities for collaboration. Through MCP, multiple AI agents collaborate through functional specialization, combining functions to complete complex tasks such as on-chain data analysis, market forecasting, and risk management, thereby increasing overall efficiency and reliability. For chain transaction automation, MCP coordinates various transaction and risk management agents to address issues such as slipping, transaction friction, and MEV (minor extractable values), allowing for safer and more efficient on-chain asset management.
3. Related projects
1. DEMCP
DEMCP is a distributed MCP network. The aim is to provide AI agents with self-developed open source MCP services, provide developers with a commercial revenue sharing platform for MCPs, and provide one-stop access to mainstream leading language models (LLMS). Developers can obtain services through Stablecoin Payments (USDT, USDC). As of May 8th, the market capitalization of the token DMCP is approximately $1.62 million.
2. Dark
Dark is an MCP network built on the Solana blockchain that runs within a trusted execution environment (TEE). That token $dark is listed in Binance Alpha, which has a market capitalization of approximately $118.1 million as of May 8th. Dark’s first application is currently under development and is designed to be an AI agent with efficient tool integration capabilities via TEE and MCP protocols, allowing developers to quickly connect to a variety of tools and external services via simple configurations. Although this product is not yet fully launched, users can take part in the early access phase via email waitlists to participate in the test and provide feedback.
3.Cookie.Fun
Cookie.fun is a dedicated platform for AI agents within the Web3 ecosystem and is designed 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 introducing metrics such as cognitive impact, adaptive intelligence capabilities, user engagement, and on-chain data. On April 24th, the Cookie.API 1.0 update introduced a dedicated MCP server with a plug-and-play agent-specific infrastructure designed for both developers and non-technical users, while requiring configuration.
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4.Skyai
SkyAI is a Web3 data infrastructure project built on the BNB chain, aiming to establish a blockchain native AI infrastructure through MCP (Model Context Protocol) extension. The platform plans to streamline the development process by providing scalable and interoperable data protocols for Web3-based AI applications and integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thus moving forward with practical AI adoption in blockchain environments. Currently, SkyAI supports aggregated datasets from BNB chains and Solana, with over 10 billion rows of data and plans for the future to launch an MCP data server that supports Ethereum Mainnet and Base Chain. Its token Skyai is listed on Binance Alpha and holds a market capitalization of approximately $42.7 million as of May 8th.
4. Future Development
As a new narrative in AI and blockchain convergence, MCP protocols demonstrate significant potential in increasing data interaction efficiency, reducing development costs, and enhancing security and privacy protection. In particular, similar scenarios that hold distributed finance (DEFI) and wide application outlook. However, most of the current MCP-based projects remain in the proof-of-concept stage, but have yet to launch any mature products. This immaturity led to a sustained decline in token prices after listing, exemplified by DEMCP tokens that plunge to 74% within a month of their debut. This trend reflects a crisis of market-wide confidence in MCP initiatives primarily due to long-term development cycles and lack of tangible real-world applications. The result is accelerated product development, ensure tight coordination between tokens and functional products, and improve the user experience as a key challenge for MCP projects. Furthermore, facilitating MCP protocols within the cryptographic ecosystem faces technical integration hurdles. Branching smart contractors and data structures across blockchain and DAPP require substantial development resources to establish a unified standardized MCP server.
Despite these challenges, the MCP protocol maintains considerable market potential. As AI technology advances and protocols mature, it could potentially enable a wider range of applications in domains such as Defi and DAOS. For example, AI agents that leverage MCP can access real-time on-chain data and execute automated transactions to improve the efficiency and accuracy of market analysis. Furthermore, MCP’s decentralized nature provides a transparent, trackable operational framework for AI models, which may promote the decentralization of AI resources and assets. Positioned as a key enabler of AI-BlockChain integration, the MCP protocol could evolve into a critical engine powered by next-generation AI agents as technology matures and use cases expands. However, achieving this vision requires overcoming many challenges, including technology integration, security assurance and user experience optimization.
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