The New Era of AI and Encryption Technology Integration: Analysis of the MCP Protocol

Chapter One AI+Crypto: The Dual Technological Wave of Accelerated Integration

Recently, the term "AI+Crypto" has become popular rapidly. From the emergence of ChatGPT to major AI companies launching multimodal large models, and then to the attempts of DeFi protocols, governance systems, and NFT platforms in the blockchain world to integrate AI agents, this technological fusion has become a new paradigm evolution that is happening in reality.

This trend originates from the complementarity of two major technological systems on the demand and supply sides. The development of AI allows machines to perform tasks and process information, but it still faces limitations such as a lack of contextual understanding, incentive structures, and credible outputs. Meanwhile, the on-chain data systems, incentive mechanism designs, and programmatic governance frameworks provided by encryption technology can precisely fill these gaps in AI. Conversely, the encryption industry also requires more powerful intelligent tools to handle repetitive tasks such as user behavior, risk management, and transaction execution, which is precisely AI's strength.

In short, encryption technology provides a structured world for AI, while AI injects active decision-making capabilities into encryption technology. This mutually foundational technological integration has formed a deep new pattern of "mutually foundational infrastructure." For example, in DeFi protocols, "AI market makers" have emerged, which model market fluctuations in real-time through AI models, combining on-chain data, order book depth, and other variables to achieve dynamic liquidity scheduling. Similarly, in governance scenarios, AI-assisted "governance agents" are beginning to attempt to interpret proposal content, predict voting tendencies, and provide personalized decision-making recommendations for users.

From a data perspective, the behavioral data on the blockchain inherently possesses the characteristics of verifiability, structure, and resistance to censorship, making it ideal material for AI model training. Some projects have already begun to attempt embedding on-chain behavior into the model fine-tuning process, and in the future, a "on-chain AI model standard" may emerge, enabling models to have native Web3 semantic understanding capabilities during training.

At the same time, the on-chain incentive mechanism provides AI systems with a more robust and sustainable economic drive than Web2 platforms. By defining the Agent incentive protocol, model executors can earn token rewards through on-chain "task execution proof + user intent fulfillment + traceable economic value." This means that AI agents can participate in the economic system for the first time, rather than merely being embedded as tools.

From a more macro perspective, this trend is not only a technological integration but also a paradigm shift. AI+Crypto may ultimately evolve into an "agent-centric on-chain social structure": models on the chain can not only execute contracts but also understand context, coordinate games, govern proactively, and establish their own micro-economies through token mechanisms.

For this reason, the narrative of AI + Crypto has recently gained significant attention in the capital markets. From well-known venture capitalists to the launch of emerging projects, there is a consensus in the industry: AI models will play a role in Web3 that is not just a "tool", but rather a "subject" — they will possess identity, context, incentives, and even governance rights.

It is foreseeable that in the future Web3 world, AI agents will be unavoidable system participants. This form of participation will gradually evolve into a new form of "model as node" and "intention as contract". Behind this lies the semantics and execution paradigm constructed by a new type of protocol, MCP(Model Context Protocol).

The integration of AI and encryption technology is one of the few "layer-to-layer docking" opportunities in the past decade. This is not a hotspot that explodes at a single point, but a long-cycle, structural evolution. It will determine how AI operates, coordinates, and is incentivized on the chain, and will ultimately define the future form of the on-chain social structure.

Chapter 2 MC Protocol: Background and Core Mechanism

The integration of AI and encryption technology is moving from conceptual exploration to practical verification stage. Since 2024, large language models have begun to possess stable context management, task decomposition, and self-learning capabilities, making it possible for AI to continuously interact and make autonomous decisions on the chain. At the same time, the encryption world itself is also evolving, and the maturity of technologies such as Modular blockchain and account abstraction has cleared the obstacles for AI to become a native participant in the blockchain.

In this context, the MCP( Model Context Protocol) was born, aiming to construct a universal protocol layer for AI models to run, execute, provide feedback, and generate revenue on-chain. This not only addresses the technical challenge of "AI cannot be efficiently used on-chain," but also responds to the systemic demand for Web3's transition to an "intention-driven paradigm." Traditional smart contract calls require users to have a high understanding of the chain's state, function interfaces, etc., which creates a significant gap with the natural expressions of ordinary users. The intervention of AI models can bridge this gap, but the premise is that it must possess "identity," "memory," "permissions," and "economic incentives" on-chain. The MCP protocol was precisely born to solve these bottlenecks.

MCP is a full-chain semantic layer protocol that spans AI model invocation, context construction, intent understanding, on-chain execution, and incentive feedback. Its core design includes:

1. Model Identity Mechanism: Each model instance or agent has an independent on-chain address, which can receive assets, initiate transactions, and invoke contracts through permission verification, becoming the "first class account" in the blockchain world.

2. Context Collection and Semantic Interpretation System: Abstracting on-chain states, off-chain data, and historical interaction records, combined with natural language input, to provide the model with a clear task structure and environmental background.

3. Task execution and verification mechanism: Transform user intentions into executable on-chain operation sequences and ensure the correctness of execution results through multi-party validation.

4. Incentives and feedback loop: Based on task completion, resource consumption, and other indicators, provide token incentives for the model and incorporate user feedback into the model's iterative optimization process.

Currently, multiple projects are working on establishing prototype systems based on the MCP concept. For example, one project is attempting to deploy AI models as publicly callable on-chain agents to serve scenarios such as trading strategy generation and asset management decision-making; another project has built a multi-agent collaboration system based on MCP, allowing multiple models to collaborate dynamically around the same user task. There are also projects trying to expand MCP into a "model operating system" foundational layer, where any developer can build specific capability model plugins on it for others to call, forming a shared on-chain AI service market.

From an investment perspective, the introduction of MCP not only brings new technological pathways but also presents an opportunity for the restructuring of industrial structure. It opens up a new "native AI economic layer" where models are not just tools but also participants in the economy with accounts, credit, returns, and evolutionary paths. This means that in the future of DeFi, market makers may be models, voting participants in DAO governance may be models, content curators in the NFT ecosystem may be models, and even the on-chain data itself could be parsed, combined, and repriced by models, giving rise to a brand new "AI behavioral data asset".

As more and more models enter the Web3 world, the closed loop of identity, context, execution, and incentives will determine whether this trend can truly take root. MCP is not a single-point breakthrough, but a "infrastructure-level protocol" that provides a consensus interface for the entire AI+Crypto wave. It attempts to answer not only the technical question of "how to put AI on the chain," but also the economic system question of "how to incentivize AI to continuously create value on the chain."

Chapter 3 Typical Application Scenarios of AI Agent: MCP Reconstruction of On-chain Task Model

When AI models possess on-chain identity, semantic context awareness, intent parsing, and on-chain task execution capabilities, they will become substantive on-chain Agents, acting as proactive entities in executing logic. This is the core significance of the MCP protocol — to provide a structured path for AI models to enter the blockchain world, interact with contracts, collaborate with humans, and engage with assets. This path includes underlying capabilities such as identity, permissions, and memory, as well as intermediate operations like task decomposition, semantic planning, and proof of performance, ultimately leading to the possibility of AI Agents participating in the construction of the Web3 economic system.

On-chain asset management is the first field penetrated by AI Agent. Based on MCP, the AI Agent can automatically crawl on-chain data after obtaining intentions such as "optimizing yield" or "controlling risk exposure," assess the risk premiums and expected volatility of different protocols, and dynamically generate trading strategy combinations. It then verifies the safety of the execution path through simulation calculations or on-chain real-time backtesting. This allows non-professional users to delegate assets using natural language, making asset management no longer an act with a very high technical threshold.

On-chain identity and social interaction is another scenario that is maturing rapidly. Users can have a "semantic agent" that continuously syncs with their preferences, interests, and behavioral dynamics, allowing the agent to participate in social DAOs, publish content, plan NFT activities, and even help users maintain their on-chain reputation and influence. Some social chains have begun deploying Agents that support the MCP protocol to assist new users in completing the onboarding process, establishing social graphs, and participating in comments and voting, transforming the "cold start problem" from a product design issue into a problem of intelligent agent participation.

Governance and DAO management are the third key focal points of the AI Agent. Agents equipped with semantic parsing and intent understanding capabilities can assist users in regularly sorting out DAO dynamics, extracting key information, providing semantic summaries of proposals, and recommending voting options or automatically executing voting actions based on an understanding of user preferences. This on-chain governance based on the "preference agent" mechanism greatly alleviates the problems of information overload and incentive misalignment. The MCP framework also allows models to share governance experiences and strategy evolution paths, forming a transfer mechanism for governance knowledge across communities, thereby building an increasingly "intelligent" governance structure.

In addition, MCP also provides unified interface possibilities for AI in scenarios such as on-chain data curation, game world interaction, ZK automatic proof generation, and cross-chain task relaying. In the blockchain game (GameFi) field, AI Agents can serve as the brains behind non-player characters (NPC), enabling real-time dialogue, storyline generation, task scheduling, and behavior evolution; in the NFT content ecosystem, models can act as "semantic curators," dynamically recommending NFT collections based on user interests, and even generating personalized content; in the ZK field, models can quickly translate intentions into ZK-friendly constraint systems through structured compilation, simplifying the zero-knowledge proof generation process and enhancing the universality of development thresholds.

The MCP protocol is changing not just the single-point performance of a specific application, but the very paradigm of task execution itself. Traditional Web3 task execution is based on the premise of "you know how to do it," where users must grasp underlying knowledge such as contract logic, transaction structure, and network fees. In contrast, MCP transforms this paradigm into "you only need to express what you want to do," leaving the rest to the model. The intermediary layer of interaction between users and the chain has shifted from a code interface to a semantic interface, from function calls to intention orchestration. This fundamental transformation elevates AI from a "tool" to an "agent," and also shifts blockchain from a "protocol network" to an "interaction context."

Chapter 4 Market Prospects and Industry Application Analysis of MCP Protocol

The MCP protocol, as an innovation that integrates AI and blockchain technology, brings a new economic model to the encryption market and provides development opportunities for multiple industries. With the advancement of AI technology and the expansion of blockchain application scenarios, the market prospects of the MCP protocol will gradually reveal its immense potential.

The integration of AI and blockchain has become an important force driving the digital transformation of the global economy. Under the promotion of the MCP protocol, AI models can not only perform tasks but also conduct value exchanges on the blockchain, becoming independent economic entities. An increasing number of AI models are beginning to undertake actual market tasks, participating in areas such as commodity production, service delivery, and financial decision-making. The decentralized, transparent, and immutable characteristics of blockchain provide an ideal trust mechanism for AI models, allowing them to be quickly applied across various industries.

In the coming years, the integration of AI and the encryption market is expected to experience explosive growth. The MCP protocol, as a pioneer of this trend, will occupy an important position in fields such as finance, healthcare, manufacturing, smart contracts, and digital asset management. The emergence of AI native assets not only creates abundant opportunities for developers and investors but also brings disruptive impacts to traditional industries.

The MCP protocol brings cross-industry integration and collaboration possibilities to multiple sectors. In the financial industry, the MCP protocol can promote the deepening of the DeFi ecosystem by providing tradeable "revenue rights" assets for AI models. Users can not only invest in the AI models themselves but also trade the revenue rights of the models on decentralized finance platforms through smart contracts. This offers investors a richer array of investment options and may encourage more traditional financial institutions to expand into the blockchain and AI fields.