Mahe, Seychelles, May 9, 2025 (Globe Newswire) – Bitmart Research, the army of research Bitmart Exchangehas issued a detailed report on the Innovative MCP+AI Agent Framework, a new paradigm for AI applications. This report dives for the progress of the context Protocol (MCP), its integration with coded AI agents and the transformative impact on blockchain automation, decentralized applications and interoperability of platform-shadable. The findings emphasize the potential of this framework to improve AI options, streamlining complex integrations and stimulating 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 been familiar with generic dialogue models that were missing personalized character institutions, which resulted in monotonous and impersonal reactions. In order to tackle this limitation, developers introduced the concept of “persona” AI with specific roles, personalities and speech patterns to better tailor reactions to the expectations of users. Even with well -defined personas, however, these systems were unable to perform passive responds proactively or to handle complex operations. This gave rise to the Open-Source Project Auto-Gpt, with which developers can define a series of tools and functions for AI systems. By registering these tools within the framework, Auto-GPT can generate operational assignments on the basis of pre-defined rules and sources when processing user requests, performing tasks and performing return results autonomously. AI transforms this progress from passive conversation means into proactive task -oriented systems.
Despite the progress of Auto-Gpt when engaging autonomous AI activities, challenges remained with regard to inconsistent information of the tools and poor compatibility with platform-dependent. The Context Protocol (MCP) model has been developed to tackle these core challenges in AI development, in particular the complexity of integrating external tools. The primary goal of MCP is to streamline AI tool interactions through standardized communication protocols, making seamless integration of various external services possible. Traditionally, the implementation of complex functionalities such as Wequeries or Web access in large language models required Extensive Custom Coding and Tool documentation – a process that considerably increased the development complexity and time investments. MCP simplifies this process fundamentally by setting up standardized interfaces and communication specifications, so that AI models can communicate with external tools more efficiently and effectively.
2. Integration of MCP and AI agent
MCP and encrypted AI agents share an additional relationship, with their most important distinction in their respective focus. AI agents mainly concentrate on blockchain automation, smart contract implementation and crypto -activa management, which emphasize privacy protection and integration with decentralized applications. MCP, inversely, gives priority to simplifying interactions between AI agents and external systems via standardized protocols and context management, which improves interoperability and flexibility of platform-dependent improvement. By using the MCP protocol, coded AI agents can achieve more efficient platform-dependent integration and operations, increasing their implementation options.
AI agents of the earlier generation owned fundamental operational capacities, such as carrying out transactions through smart contracts and managing portfolios. However, these functions were usually defined in advance, without flexibility and adaptability. The core value of MCP lies in determining uniform communication standards for interactions between AI agents and external tools-included blockchain data, smart contracts and off-chain services. This standardization deals with traditional development challenges of interface fragmentation, so that AI agents can be seamlessly integrated with multiple chain data and aids, while their autonomous implementation options are considerably improved. For example, Defi-oriented AI agents who use MCP, for example, have access to real-time market data and automatically optimizing investment portfolios. In addition, MCP unlocks new cooperation options: Via MCP, multiple AI agents can work together through functional specialization, combining opportunities to complete complex tasks, such as data analysis on chains, market forecast and risk management, which improves overall efficiency and reliability. For transaction automation on the chain, MCP orchestres various trade and risk management agents to tackle problems such as slippery, transactions and MEV (miner extractable value), making it possible to safer and more efficiently on-chain assets management.
3. Related projects
1. DEMCP
DEMCP is a decentralized MCP network. It is intended to offer yourself developed MCP services with open-source for AI agents, to offer developers a commercial income exchange platform for MCP and to enable One-Stop access to regular large language models (LLMS). Developers can acquire services via Stablecoin payments (USDT, USDC). From May 8, its token DMCP has a market capitalization of approximately $ 1.62 million.
2. Days
Dark is an MCP network that works in Trusted Execution environments (TEE), built on the Solana Blockchain. The token $ dark is mentioned on Binance Alpha, with a market capitalization of around $ 118.1 million from 8 May. Currently, the first application of Dark is being developed, designed to authorize AI agents with efficient possibilities for the integration of tools via TEE and the MCP protocol, which enables developers to make quick contact with various tools and external services. Although the product has not yet been fully launched, users can participate in the Early Access phase via an e -mail waiting list to participate in testing and giving feedback.
3. Cookie.fun
Cookie.Fun is a platform dedicated to AI agents within the web3 -ecosystem, designed to offer users an extensive AI agent index and analytics toolkit. The platform helps users to understand and evaluate the performance of different AI agents by presenting statistics such as cognitive influence, adaptive intelligence options, user involvement and data on chains. On 24 April the cookie.api 1.0 update introduced a special MCP server with plug-and-play agent-specific infrastructure, designed for both developers and non-technical users, while they do not require configuration.
Data source:X
4.SKYAI
Skyai is a web3 data infrastructure project built on the BNB chain, with the aim of setting up blockchain-native AI infrastructure via MCP (model context protocol) extension. The Platform offers scalable and interoperable data protocols for web3-based AI applications, planning to streamline development processes by integrating multi-chain data access, AI agent implementation and protocol level utility programs, promoting AI acceptance in blockchain males. Skyai is currently supporting aggregated datasets from BNB chain and Solana, more than 10 billion rows of data, with future plans to launch MCP scene servers to support Ethereum Manet and basic chain. The Token Skyai is listed on Binance Alpha and will hold a market capitalization of approximately $ 42.7 million from 8 May.
4. Development
The MCP protocol, as an emerging story in the convergence of AI and Blockchain, demonstrates considerable potential when improving the efficiency of data interaction, reducing development costs and strengthening security and privacy protection – mainly in decentralized finances (Defi) contains the broad -place – proof. However, most current MCP-based projects remain in the proof-of-concept phase, which still have to launch mature products. This immaturity has led to ongoing decreases of token prices after the list, illustrated by the DEMCP token that fell 74% within one month after his debut. This trend reflects a market-wide crisis of trust in MCP initiatives, mainly as a result of long-term development cycles and the absence of tangible Real-World applications. Consequently, accelerating product development, guaranteeing strict coordination between tokens and functional products and improving the user experience arises as critical challenges for MCP projects. In addition, promoting the MCP protocol within the crypto ecosystem is confronted with technical integration nuisances. Divergent Smart Contract Logic and data structures between block chains and Dapp’s require substantial development sources to set up uniform, standardized MCP servers.
Despite these challenges, the MCP protocol retains a considerable market potential. As the AI technology progresses and the protocol matures, it can make broader applications in domains such as Defi and DAOS possible. AI agents who use MCP can, for example, gain access to real-time on-chain data to carry out automated transactions, to improve the efficiency and accuracy of the market analysis. In addition, the decentralized nature of MCP AI models can offer with transparent, traceable operational frameworks that promote the decentralization and assetization of AI resources. The MCP protocol, positioned as a key enabler of AI-blockchain integration, could evolve into a vital engine that drives the next generation of AI agents as technology ripens and expand use cases. However, realizing this vision requires the overcoming versatile challenges, including technical integration, safety assurance and optimization of user experience.
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