Mahe, Seychelles , May 09, 2025 (GLOBE NEWSWIRE) — BitMart Research, the research arm of BitMart Exchange, has released a detailed report on the innovative MCP+AI Agent framework, a new paradigm for AI applications. This report delves into the advancements of the Model Context Protocol (MCP), its integration with encrypted AI agents, and the transformative impact on blockchain automation, decentralized applications, and cross-platform interoperability. The findings highlight the potential of this framework to enhance AI capabilities, streamline complex integrations, and drive the future of AI in the blockchain ecosystem.
1. Introduction to the concept of MCP
In the field of artificial intelligence, traditional chatbots have long relied on generic dialogue models that lacked personalized character settings, resulting in monotonous and impersonal responses. To address this limitation, developers introduced the concept of “persona” – endowing AI with specific roles, personalities, and speech patterns to better align responses with user expectations. However, even with well-defined personas, these systems remained passive responders incapable of proactively executing tasks or handling complex operations. This gave rise to the open-source project Auto-GPT, which enables developers to define a suite of tools and functions for 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, autonomously executing tasks and returning results. This advancement transforms AI from passive conversational agents into proactive task-oriented systems.
Despite Auto-GPT’s progress in enabling autonomous AI operations, challenges persisted regarding inconsistent tool invocation formats and poor 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. MCP’s primary objective is to streamline AI-tool interactions through standardized communication protocols, enabling seamless integration of diverse external services. Traditionally, implementing complex functionalities like weather queries or web access in large language models required extensive custom coding and tool documentation – a process that significantly increased development complexity and time investment. MCP fundamentally simplifies this process by establishing standardized interfaces and communication specifications, allowing AI models to interact with external tools more efficiently and effectively.
2. Integration of MCP and AI Agent
MCP and encrypted AI Agents share a complementary relationship, with their key distinction lying in their respective focuses. AI Agents primarily concentrate on blockchain automation, smart contract execution, and crypto asset management, emphasizing privacy protection and integration with decentralized applications. MCP, conversely, prioritizes simplifying interactions between AI Agents and external systems through standardized protocols and context management, enhancing cross-platform interoperability and flexibility. By leveraging the MCP protocol, encrypted AI Agents can achieve more efficient cross-platform integration and operations, thereby boosting their execution capabilities.
Previous-generation AI Agents possessed basic operational capacities such as executing transactions through smart contracts and managing wallets. However, these functions were typically predefined, lacking flexibility and adaptability. The core value of MCP lies in establishing unified communication standards for interactions between AI Agents and external tools – including blockchain data, smart contracts, and off-chain services. This standardization addresses traditional development challenges of interface fragmentation, enabling AI Agents to seamlessly integrate with multi-chain data and tools while significantly enhancing their autonomous execution capabilities. For instance, DeFi-focused AI Agents utilizing MCP can access real-time market data and automatically optimize investment portfolios. Furthermore, MCP unlocks novel collaborative possibilities: through MCP, multiple AI Agents can collaborate through functional specialization, combining capabilities to complete 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 orchestrates various trading and risk control Agents to address issues like slippage, transaction friction, and MEV (Miner Extractable Value), enabling 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, offer developers a commercial revenue-sharing deployment platform for MCP, and enable 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 holds a market capitalization of approximately $1.62 million.
2.DARK
DARK is an MCP network operating within Trusted Execution Environments (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, designed to empower AI Agents with efficient tool integration capabilities through TEE and the MCP protocol, enabling developers to rapidly connect with diverse tools and external services via simple configurations. Though the product has not yet fully launched, users can join the early access phase through an email waitlist to participate in testing and provide feedback.
3.Cookie.fun
Cookie.fun is a platform dedicated to AI Agents within the Web3 ecosystem, 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 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 while requiring no configuration.
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4.SkyAI
SkyAI is a Web3 data infrastructure project built on BNB Chain, aiming to establish blockchain-native AI infrastructure through MCP (Model Context Protocol) expansion. The platform provides scalable and interoperable data protocols for Web3-based AI applications, planning to streamline development processes by integrating multi-chain data access, AI agent deployment, and protocol-level utilities, thereby advancing practical AI adoption in blockchain environments. Currently, SkyAI supports aggregated datasets from BNB Chain and Solana, exceeding 10 billion rows of data, with future plans to launch MCP data servers supporting Ethereum mainnet and Base chain. Its token SkyAI is listed on Binance Alpha, holding a market capitalization of approximately $42.7 million as of May 8.
4.Future Development
The MCP protocol, as an emerging narrative in the convergence of AI and blockchain, demonstrates significant potential in enhancing data interaction efficiency, reducing development costs, and strengthening security and privacy protection—particularly in decentralized finance (DeFi) and similar scenarios where it holds broad application prospects. However, most current MCP-based projects remain in the proof-of-concept phase, having yet to launch mature products. This immaturity has led to sustained declines in token prices post-listing, exemplified by the DeMCP token plunging 74% within a month of its debut. This trend reflects a market-wide crisis of confidence in MCP initiatives, primarily stemming from prolonged development cycles and the absence of tangible real-world applications. Consequently, accelerating product development, ensuring tight alignment between tokens and functional products, and improving user experience emerge as critical challenges for MCP projects. Additionally, promoting the MCP protocol within the crypto ecosystem faces technical integration hurdles. Divergent smart contract logic and data structures across blockchains and DApps necessitate substantial development resources to establish unified, standardized MCP servers.
Despite these challenges, the MCP protocol retains considerable market potential. As AI technology advances and the protocol matures, it could enable broader applications in domains like DeFi and DAOs. For instance, AI agents leveraging MCP could access real-time on-chain data to execute automated transactions, enhancing market analysis efficiency and accuracy. Moreover, MCP’s decentralized nature may provide AI models with transparent, traceable operational frameworks, fostering the decentralization and assetization of AI resources. Positioned as a key enabler of AI-blockchain integration, the MCP protocol could evolve into a vital engine powering next-generation AI agents as technology matures and use cases expand. However, realizing this vision requires overcoming multifaceted challenges including technical integration, security assurance, and user experience optimization.
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