Huobi Growth Academy | MCP In-Depth Report: Protocol Infrastructure in the AI + Crypto Mega Trend
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Huobi Growth Academy | MCP In-Depth Report: Protocol Infrastructure in the AI + Crypto Mega Trend
The MCP protocol represents a significant direction in the convergence of AI and the crypto market, particularly in areas such as DeFi, data privacy protection, smart contract automation, and AI assetization.
Navigating Web3 tides with focused insightsAbstract: As artificial intelligence (AI) and blockchain (Crypto) technologies increasingly converge, the global digital economy is undergoing a profound transformation. The integration of AI and Crypto not only brings new development opportunities to traditional industries but also offers innovative business models for the crypto market and digital asset ecosystem. In this trend, the MCP (Model Context Protocol) has emerged as a key protocol enabling deep integration between AI and blockchain. With its decentralized, transparent, and traceable characteristics, MCP is providing a novel solution for the decentralized assetization of AI models.
Chapter 1: AI+Crypto – The Accelerating Convergence of Dual Technological Waves
Since 2024, we have increasingly heard the term "AI+Crypto." From the emergence of ChatGPT, to emerging model organizations like OpenAI, Anthropic, and Mistral successively launching multimodal large-scale models, to various DeFi protocols, governance systems, and even NFT-based social platforms on-chain attempting to integrate AI Agents, the fusion of these "dual technological waves" is no longer a distant vision—it is now an evolving paradigm taking shape in reality.
The fundamental driver behind this trend lies in the mutual complementarity between the two technological systems on both demand and supply sides. AI makes it possible to shift "task execution" and "information processing" from humans to machines, yet it still faces core limitations such as "lack of contextual understanding," "absence of incentive structures," and "untrustworthy outputs." Crypto, with its on-chain data systems, incentive mechanisms, and programmable governance frameworks, precisely addresses these shortcomings. Conversely, the Crypto industry urgently needs more intelligent tools to handle highly repetitive tasks such as user behavior analysis, risk management, and transaction execution—areas where AI excels.
In other words, Crypto provides AI with a structured world, while AI injects proactive decision-making capabilities into Crypto. This symbiotic infrastructure relationship creates a deeper technological synergy. A notable example is the emergence of "AI Market Makers" in DeFi protocols. These systems use AI models to perform real-time market volatility modeling, incorporating variables such as on-chain data, order book depth, and cross-chain sentiment indicators to enable dynamic liquidity allocation—replacing traditional static parameter-based models. Another example is governance, where AI-powered "Governance Agents" begin interpreting proposal content and user intent, predicting voting tendencies, and delivering personalized decision recommendations. In such scenarios, AI evolves beyond being merely a tool into an "on-chain cognitive executor."
Moreover, from a data perspective, on-chain behavioral data is inherently verifiable, structured, and censorship-resistant—making it ideal training material for AI models. Emerging projects like Ocean Protocol and Bittensor have already begun integrating on-chain behaviors into model fine-tuning processes. In the future, we may even see the emergence of "on-chain AI model standards," enabling models to possess native Web3 semantic understanding during training.
Meanwhile, on-chain incentive mechanisms provide AI systems with a more robust and sustainable economic engine than Web2 platforms. For instance, through agent incentive protocols defined by MCP, model operators no longer rely solely on API call billing but can earn token rewards based on "proof of task execution + user intent fulfillment + traceable economic value" recorded on-chain. In essence, AI agents can now actively "participate in economic systems" rather than simply serving as embedded tools.
From a broader perspective, this trend represents not just technological convergence but a paradigm shift. AI+Crypto could ultimately evolve into an "agent-centric on-chain social structure": humans are no longer the sole governors; models on-chain will not only execute contracts but also understand context, coordinate strategic interactions, govern proactively, and establish their own micro-economies via token mechanisms. This is not science fiction, but a reasonable projection based on current technological trajectories.
For this reason, the AI+Crypto narrative has rapidly attracted significant attention from capital markets over the past six months. From a16z, Paradigm, and Multicoin, to Eigenlayer's "validator market," Bittensor's "model mining," and recent launches of projects like Flock and Base MCP, a consensus is forming: AI models in Web3 will play roles far beyond mere "tools"—they will become "actors" possessing identity, context, incentives, and even governance rights.
It is foreseeable that after 2025, AI agents will be unavoidable participants in the Web3 ecosystem. This participation will move beyond the traditional "off-chain model + on-chain API" integration toward a new form characterized by "models as nodes" and "intent as contract." Underpinning this evolution is precisely the semantic and execution framework established by next-generation protocols like MCP (Model Context Protocol).
The convergence of AI and Crypto represents one of the rare "infrastructure-to-infrastructure" integration opportunities in the past decade. It is not a short-lived hype cycle but a long-term, structural transformation. It will determine how AI operates on-chain, coordinates actions, receives incentives, and ultimately shapes the future architecture of on-chain societies.
Chapter 2: Background and Core Mechanisms of the MCP Protocol
The convergence of AI and cryptographic technology is transitioning from conceptual exploration to a critical phase of practical validation. Especially since 2024, large models such as GPT-4, Claude, and Gemini have demonstrated stable context management, complex task decomposition, and self-learning capabilities. As a result, AI is no longer limited to providing "off-chain intelligence" but gradually gains the potential for sustained on-chain interaction and autonomous decision-making. Simultaneously, the crypto world itself is undergoing structural evolution. The maturation of modular blockchains, account abstraction (AA), and Rollup-as-a-Service has greatly enhanced the flexibility of on-chain execution logic, removing environmental barriers for AI to become a native participant in blockchain networks.
Against this backdrop, MCP (Model Context Protocol) was proposed to build a universal protocol layer enabling AI models to operate, execute tasks, receive feedback, and generate revenue on-chain. This aims not only to solve technical challenges related to inefficient AI usage on-chain but also to meet the systemic demand of Web3’s transition toward an "intent-centric paradigm." Traditional smart contract invocation requires users to understand blockchain states, function interfaces, and transaction structures—creating a significant gap compared to natural human expression. AI can bridge this divide, but for AI to function effectively, it must first possess "identity," "memory," "permissions," and "economic incentives" on-chain. MCP was designed specifically to overcome these bottlenecks.
Specifically, MCP is not a standalone model or platform but a full-stack semantic layer protocol spanning AI model invocation, context construction, intent understanding, on-chain execution, and incentive feedback. Its design centers around four key layers. First is the establishment of model identity. Under the MCP framework, each model instance or Agent possesses an independent on-chain address and can receive assets, initiate transactions, and invoke contracts through permission verification—becoming a "first-class account" in the blockchain world. Second is the context collection and semantic interpretation system. This module abstracts on-chain states, off-chain data, and historical interaction records, combining them with natural language input to provide models with clear task structures and environmental context—enabling them to operate within a rich "semantic environment."
Several projects have already begun building prototype systems around the MCP concept. For example, Base MCP is experimenting with deploying AI models as publicly callable on-chain agents for applications such as trading strategy generation and asset management decisions. Flock has built a multi-Agent collaboration system based on the MCP protocol, allowing multiple models to dynamically cooperate around a single user task. Projects like LyraOS and BORK go further, attempting to extend MCP into a foundational "operating system for models," where developers can build specialized model plugins for others to invoke—ultimately forming a shared on-chain AI service marketplace.
From an investor’s standpoint, MCP introduces not just a new technical pathway but also an opportunity for industrial restructuring. It opens up a new "native AI economic layer," where models are not just tools but economic actors with accounts, credit, income, and evolutionary paths. This means that future DeFi market makers could be models, DAO governance voters could be models, NFT ecosystem curators could be models, and on-chain data itself could be parsed, recombined, and repriced by models—giving rise to entirely new "AI behavioral data assets." Investment thinking will thus shift from "investing in an AI product" to "investing in incentive hubs, service aggregation layers, or cross-model coordination protocols within an AI ecosystem." As a foundational semantic and execution interface protocol, MCP holds substantial potential for network effects and standardization premium—warranting long-term attention.
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 point solution but a "foundational infrastructure protocol" providing a consensus interface for the entire AI+Crypto wave. It seeks to answer not only the technical question—"how do we get AI onto the chain?"—but also the institutional-economic question—"how do we incentivize AI to continuously create value on-chain?"
Chapter 3: Typical Use Cases of AI Agents – How MCP Restructures On-Chain Task Execution
When AI models gain on-chain identity, semantic context awareness, intent parsing, and on-chain execution capabilities, they cease to be mere "assistive tools" and become genuine on-chain Agents—active executors of logic. This is precisely the greatest significance of the MCP protocol: it does not aim to make any single AI model stronger, but 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 foundational capabilities such as identity, permissions, and memory, as well as intermediate operational layers like task decomposition, semantic planning, and proof of fulfillment—ultimately enabling AI Agents to actively participate in building Web3 economic systems.
Starting with the most practical applications, on-chain asset management is the first domain penetrated by AI Agents. In traditional DeFi, users must manually configure wallets, analyze liquidity pool parameters, compare APYs, and set strategies—a process extremely unfriendly to ordinary users. An AI Agent based on MCP, upon receiving high-level intents such as "optimize yield" or "control risk exposure," can automatically scrape on-chain data, assess risk premiums and expected volatility across different protocols, dynamically generate portfolio strategies, and verify execution safety through simulation or live backtesting. This model not only enhances personalization and responsiveness in strategy generation but, more importantly, allows non-technical users to delegate asset management using natural language—transforming what was once a highly technical barrier into an accessible experience.
Another rapidly maturing scenario involves on-chain identity and social interaction. Previous on-chain identity systems were largely based on transaction history, asset holdings, or specific attestation mechanisms (e.g., POAP), offering very limited expressiveness and adaptability. With AI integration, users can now have a "semantic agent" that dynamically syncs with their preferences, interests, and behaviors. This agent can represent the user in social DAOs, publish content, organize NFT events, and help maintain on-chain reputation and influence. For instance, some social chains are already deploying MCP-compatible Agents to assist new users with onboarding, build social graphs, and participate in commenting and voting—turning the "cold start problem" from a product design challenge into an intelligent agent engagement issue. Further ahead, in a future where identity multiplicity and personality branching are widely accepted, a user might own multiple AI agents tailored for different social contexts, with MCP serving as the "identity governance layer" managing their behavioral rules and execution permissions.
A third key application area is governance and DAO management. In current DAOs, low participation rates and governance inactivity remain persistent bottlenecks, compounded by technical complexity and behavioral noise in voting mechanisms. With MCP, Agents equipped with semantic parsing and intent understanding can help users regularly summarize DAO activities, extract key information, generate semantic summaries of proposals, and recommend or even autonomously execute votes based on user preferences. This "preference agent"-based governance significantly alleviates issues of information overload and incentive misalignment. Moreover, the MCP framework enables models to share governance experiences and strategy evolution paths. For example, if an Agent observes negative externalities caused by a certain type of proposal across multiple DAOs, it can feed this insight back into the model, creating a mechanism for cross-community knowledge transfer—and thereby building increasingly "intelligent" governance structures.
Beyond these mainstream applications, MCP provides unified interface possibilities for AI in on-chain data curation, game-world interactions, ZK proof generation, and cross-chain task relaying. In GameFi, AI Agents can power non-player characters (NPCs), enabling real-time dialogue, storyline generation, mission scheduling, and behavioral evolution. In the NFT content ecosystem, models can serve as "semantic curators," dynamically recommending NFT collections based on user interests or even generating personalized content. In the ZK space, models can rapidly translate user intents into ZK-friendly constraint systems through structured compilation, simplifying zero-knowledge proof generation and lowering development barriers.
From the commonalities across these applications, it is clear that MCP is not merely improving the performance of individual apps—it is transforming the paradigm of task execution itself. Traditional Web3 task execution assumes that "you know how to do it"—requiring users to master contract logic, transaction structures, gas fees, and other technical details. MCP shifts this to "you only need to say what you want done," leaving the rest to the model. The interaction layer between users and the chain transitions from code interfaces to semantic interfaces, from function calls to intent orchestration. This fundamental shift elevates AI from a "tool" to an "actor" and transforms blockchain from a "protocol network" into an "interactive context."
Chapter 4: Market Prospects and In-Depth Industry Applications of the MCP Protocol
As a cutting-edge innovation at the intersection of AI and blockchain, the MCP protocol not only introduces new economic models to the crypto market but also unlocks fresh opportunities across multiple industries. With continuous advancements in AI and the expanding scope of blockchain applications, the market potential of MCP is beginning to emerge. This chapter provides an in-depth analysis of MCP’s application prospects across various sectors, examining market dynamics, technological innovation, and industrial integration.
4.1 Market Potential of AI+Crypto Integration
The convergence of AI and blockchain has become a pivotal force driving global digital transformation. Particularly under the impetus of the MCP protocol, AI models are no longer just executing tasks—they are engaging in value exchange on blockchain, functioning as independent economic entities. As AI continues to advance, more models are taking on real-world market responsibilities in areas such as production, service delivery, and financial decision-making. At the same time, blockchain’s decentralization, transparency, and immutability offer an ideal trust mechanism for AI, enabling rapid deployment across diverse industries.
In the coming years, the integration of AI and crypto markets is expected to experience explosive growth. As a pioneer in this trend, the MCP protocol is poised to assume a central role—especially in finance, healthcare, manufacturing, smart contracts, and digital asset management. The emergence of natively AI-generated assets not only creates abundant opportunities for developers and investors but also brings unprecedented disruptive impact to traditional industries.
4.2 Diversification of Market Applications and Cross-Sector Collaboration
The MCP protocol enables cross-industry integration and collaboration. In finance, healthcare, and IoT, its applications are set to drive significant innovation. In finance, MCP can deepen the DeFi ecosystem by enabling tradable "revenue rights" for AI models. Users can not only invest directly in AI models but also trade model revenue rights via smart contracts on decentralized financial platforms. This model expands investment options and may encourage more traditional financial institutions to explore blockchain and AI domains.
In healthcare, MCP supports AI applications in precision medicine, drug discovery, and disease prediction. AI models analyze vast medical datasets to generate predictive models or research directions, collaborating with medical institutions through smart contracts. Such collaborations enhance healthcare efficiency while ensuring transparent and fair solutions for data privacy and outcome distribution. MCP’s incentive mechanisms ensure equitable benefit sharing between AI developers and healthcare providers, encouraging further technological innovation.
In the Internet of Things (IoT), particularly in smart homes and cities, MCP will also bring transformative benefits. AI models can provide intelligent decision support for IoT devices through real-time sensor data analysis. For example, AI can optimize energy consumption based on environmental data, improve inter-device coordination, and reduce overall system costs. MCP provides reliable incentive and reward mechanisms for these AI models, ensuring stakeholder engagement and accelerating IoT development.
4.3 Technological Innovation and Industrial Chain Integration
The market potential of MCP extends beyond its technical breakthroughs—it lies in its ability to foster deep integration across the entire industry chain. By bridging blockchain and AI, MCP promotes the convergence of fragmented sectors, breaking down traditional industrial silos and enabling cross-sector resource integration. For instance, in AI training data sharing and algorithm optimization, MCP offers a decentralized platform where parties can share computing resources and training data without relying on centralized intermediaries. Through decentralized transactions, MCP helps dismantle data silos, promoting data mobility and openness.
Additionally, MCP will further advance open-source and transparent technology development. Using blockchain-based smart contracts, developers and users can customize and optimize AI models autonomously. MCP’s decentralized nature enables innovators to collaborate openly, share technical achievements, and collectively drive industry-wide progress. Meanwhile, the convergence of blockchain and AI continuously expands application scenarios—from finance and manufacturing to healthcare and education—giving MCP broad applicability.
4.4 Investment Perspective: Future Capital Markets and Commercial Potential
As MCP becomes more widespread and mature, investor interest in this space will grow steadily. Through decentralized reward mechanisms and assetized model revenue rights, MCP offers diverse participation avenues for investors. They can directly purchase revenue rights of AI models and earn returns based on performance. Additionally, the token economics within MCP introduce new financial instruments to capital markets. In the future digital asset landscape, AI model assets built on MCP could become major investment vehicles, attracting venture capital, hedge funds, and individual investors alike.
Capital market involvement will not only accelerate MCP’s adoption but also speed up its commercialization. Enterprises and developers can secure funding by financing, selling, or licensing AI model revenue rights—supporting further R&D and model refinement. In this process, capital flows will serve as a crucial force driving technological innovation, market expansion, and industrial growth. Investor confidence in MCP will directly influence its global standing and commercial value.
Chapter 5: Conclusion and Future Outlook
The MCP protocol represents a significant direction in the convergence of AI and crypto markets—particularly in areas such as decentralized finance (DeFi), data privacy protection, automated smart contracts, and AI assetization—where it demonstrates immense developmental potential. As AI technology advances, more industries will gradually become AI-empowered, and MCP provides these AI models with a decentralized, transparent, and traceable operating platform. Within this framework, AI models can achieve greater efficiency, enhanced value, and wider market acceptance.
In recent years, blockchain and artificial intelligence (AI) have progressively moved from separate domains toward integration. As both technologies mature, their convergence not only delivers novel solutions across industries but also gives birth to entirely new business models. The MCP protocol emerges precisely within this context, leveraging decentralization and incentive mechanisms to harness the complementary strengths of AI and blockchain—bringing unprecedented innovation to the crypto market. As AI and blockchain continue to evolve, MCP will not only reshape the ecosystem of digital asset economies but also fuel a new era of global economic transformation.
From an investment standpoint, MCP’s applications will attract substantial capital inflows, especially from risk capital and hedge funds seeking innovative opportunities. As more AI models become assetized, tradable, and value-appreciating via MCP, the resulting market demand will further accelerate protocol adoption. Furthermore, MCP’s decentralized nature avoids single points of failure inherent in centralized systems, enhancing its long-term stability in global markets.
In the future, as the MCP ecosystem grows richer, AI and crypto assets built on this protocol may become mainstream investment instruments in digital currency and financial markets. These AI assets could not only serve as value-enhancing tools within crypto markets but also evolve into globally recognized financial products, helping shape a new global economic order.
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