In-Depth Report on MCP: New Protocol Infrastructure in the Age of AI + Crypto

Summary:

As artificial intelligence (AI) and blockchain (Crypto) technologies continue to converge, the global digital economy is ushering in a profound transformation. The combination of AI and Crypto is not only creating new development opportunities for traditional industries but also giving rise to entirely new business models in the crypto market and the digital asset field. Amid this trend, MCP (Model Context Protocol) has emerged as a key protocol driving the deep integration of AI and blockchain, offering brand-new solutions for transforming AI models into digital assets with its decentralized, transparent, and traceable features.

Chapter 1: AI + Crypto –– The Two Fast Converging Waves

Since 2024, the phrase "AI + Crypto" has been increasingly frequently mentioned. ChatGPT's breakthrough success, the rollout of multimodal supermodels by emerging institutions specializing in models like OpenAI, Anthropic, and Mistral, and the growing adoption of AI agents by various on-chain DeFi protocols, governance systems, and even NFT-based social platforms all signal that the convergence of these "dual technological waves" is no longer a distant vision, but a paradigm shift already underway.

The fundamental driving force behind this trend is the complementary dynamic between two major technological systems on both the demand and supply side. The development of AI has made it possible for machines to execute tasks and process information instead of humans, though it still struggles with fundamental limitations such as a lack of contextual understanding, insufficient incentive structures, and unreliable output. Crypto, on the other hand, offers on-chain data systems, incentive design mechanisms, and programmable governance frameworks that can address these shortcomings of AI. Conversely, the crypto space urgently requires more intelligent tools to manage highly repetitive tasks like user behavior analysis, risk control, and trade execution. These are exactly the domains in which AI excels.

In other words, Crypto provides a structured world for AI, while AI brings the Crypto space proactive decision-making capabilities. The integration of these two mutually reinforcing technologies is giving rise to a new paradigm in which each technology becomes foundational infrastructure for the other. A prime example is the emergence of "AI market makers" in DeFi protocols. These systems leverage AI to model market fluctuations in real time, while incorporating variables such as on-chain data, order book depth, and cross-chain sentiment to dynamically allocate liquidity, thus replacing traditional static parameter models. Moreover, in governance, AI-assisted "governance agents" are beginning to interpret proposals, infer user intentions, predict voting outcomes, and provide users with personalized decision recommendations. In such scenarios, AI is not a mere tool but has gradually evolved into an "on-chain cognitive executor".

More than that, 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 (such as Ocean Protocol and Bittensor) are already exploring ways to embed on-chain behavior into model fine-tuning processes. In the future, there may even be "on-chain AI model standards", empowering models with the ability of native Web3 semantic understanding during training.

At the same time, on-chain incentive mechanisms offer AI systems a more robust and sustainable economic driver than traditional Web2 platforms. For example, agent incentive protocols, defined by MCP, allow model executors to earn token rewards not through API billing but through on-chain "proofs of task execution + fulfillment of user intent + traceable economic value". In other words, AI agents can "participate in an economy" rather than merely serve as embedded tools for the first time.

On a broader scale, this trend signifies not just a technological integration but a paradigm shift. AI + Crypto may ultimately evolve into an "on-chain social structure centered around agents", where humans are no longer the sole administrators, and models will not only execute smart contracts on-chain but also interpret context, coordinate strategies, govern on their own initiatives, and build their own micro-economies through tokenomics. This is not science fiction but a logical projection based on our current trajectory of technological development.

This is precisely why the "AI + Crypto" narrative has rapidly captured the attention of capital markets over the past six months. From a16z and Paradigm to Multicoin, from Eigenlayer's "validator market" to Bittensor's "model mining", and now to the recent launch of projects like Flock and Base MCP, we are witnessing a growing consensus: In Web3, AI models will no longer be seen merely as tools but as agents that have identity, context, incentives, and even governance rights.

It is foreseeable that after 2025, AI agents will become indispensable participants in the Web3 ecosystem. Their participation will not follow the traditional integration model of "off-chain model + on-chain API" but will instead evolve into a new paradigm where "models are nodes" and "intents are contracts". Behind this lies the semantics and execution paradigms established by a new class of protocols like MCP (Model Context Protocol).

The convergence of AI and Crypto represents one of the rare "foundation-to-foundation" integrations over the past decade. It is not a sudden surge in a specific area but a long-term, structural evolution. It will determine how AI operates on-chain, how it coordinates, and how it is incentivized, ultimately shaping the future structure of on-chain societies.

Chapter 2: Background and Core Mechanisms of MCP

The convergence of AI and blockchain technology is now progressing beyond conceptual exploration into a critical phase of practical validation. In particular, since 2014, large models represented by GPT-4, Claude, and Gemini have developed stable context management, complex task decomposition, and self-learning capabilities. As a result, AI is no longer limited to offering "off-chain intelligence" but is increasingly capable of continuous interaction and autonomous decision-making on-chain. Meanwhile, the crypto world itself is undergoing structural evolution. The maturation of technologies like modular blockchains, account abstraction, and Rollup-as-a-Service has considerably enhanced the flexibility of on-chain execution logic, clearing the way for AI to become a native participant in blockchains.

Against this backdrop, the MCP (Model Context Protocol) was proposed, aiming to build a general protocol layer that supports the full lifecycle of AI models, covering on-chain operations, execution, feedback, and rewards. This is not only intended to overcome the technical barriers hindering the "effective utilization of AI on-chain" but also to meet the systematic demand arising from Web3's shift toward an "intent-centric paradigm". Traditional smart contract invocation requires users to have a deep understanding of blockchain states, function interfaces, and transaction structures, creating a sharp disconnect from the natural way ordinary users express intent. AI models can bridge this structural gap. However, for these models to play a role, they must be endowed with on-chain "identity", "memory", "permissions", and "economic incentives". MCP was precisely created to address these bottlenecks.

Specifically, MCP is not a standalone model or platform. Rather, it is a semantic protocol layer that spans the entire blockchain, covering AI model invocation, context construction, intent interpretation, on-chain execution, and incentive feedback. Its design revolves around four key components: The first is the establishment of a model identity mechanism. Under the MCP framework, each model instance or agent is assigned an independent on-chain address and can receive assets, initiate transactions, and call smart contracts through permission verification mechanisms, thereby becoming a "first-class account" in the blockchain world. The second is the context collection and semantic interpretation system. This module provides models with a clear task structure and environmental background through the abstraction of on-chain states, off-chain data, and historical interaction records, in combination with natural language inputs. This equips them with the "semantic context" necessary to execute complex instructions.

At present, multiple projects have begun developing prototype systems based on MCP. For instance, Base MCP is exploring ways to deploy AI models as publicly callable on-chain agents to serve use cases such as trading strategy generation and asset management decisions. Flock has built a multi-agent collaboration system based on MCP, enabling multiple models to dynamically coordinate around a single user task. Meanwhile, projects like LyraOS and BORK are taking MCP even further by expanding it into the foundational layer of a "model operating system", where any developer can create modular plugins for specific capabilities and make them available for others to all, ultimately forming a shared market for on-chain AI services.

From the viewpoint of crypto investors, the introduction of MCP brings about more than just a new technical path. It also presents an opportunity to restructure the industry. It unlocks a new "native AI economic layer", where models are no longer just tools but also economic participants with accounts, credit, revenue, and evolutionary paths. This means that in the future DeFi landscape, market makers, DAO governance voters, and NFT content curators could all be models, and even on-chain data could be interpreted, recombined, and repriced by models. This would give rise to an entirely new category of "AI behavioral data assets". As a result, investment strategies will also shift from "investing in a single AI product" to "investing in incentive hubs, service aggregation layers, or cross-model invocation protocols within the AI ecosystem". As the foundational protocol bridging semantics and execution, MCP warrants medium- to long-term attention for its potential network effects and the premium tied to setting industry standards.

As more models enter the Web3 world, the closed loop between identity, context, execution, and incentive will determine whether this trend can truly take hold. MCP is not about a breakthrough in a single area but an "infrastructure-level protocol" designed to provide a consensus interface for the entire AI + Crypto wave. It seeks to address not only the technical challenge of "how to bring AI on-chain" but also the economic and institutional challenge of "how to incentivize AI to continuously create value on-chain".

Chapter 3: Practical Use Cases of AI Agents –– How MCP Reshapes On-Chain Task Models

Once AI models truly possess on-chain identity, semantic context understanding, intent interpretation, and task execution capabilities, they are no longer mere auxiliary tools but de facto on-chain agents, becoming autonomous actors capable of executing logic. This is where MCP's greatest significance lies. It is not designed to make any single AI model stronger but to create a structured pathway for AI models to enter the blockchain world, interact with contracts, collaborate with humans, and engage with digital assets. This pathway encompasses foundational capabilities such as identity, permissions, and memory, as well as operational layers like task decomposition, semantic planning, and proof of execution. All these ultimately make it possible for AI agents to participate in building the Web3 economic system.

On-chain asset management is the first area where AI agents infiltrate, as it offers the most practical application. In the past, DeFi users had to manually configure wallets, analyze liquidity pool parameters, compare APYs, and set up strategies. This process is extremely unfriendly to average users. By contrast, MCP-based AI agents can automatically crawl on-chain data, assess risk premiums and expected volatility across different protocols, and dynamically generate a portfolio of trading strategies based on intents such as "optimize APYs" or "control risk exposure". The security of the execution path is then verified through simulation or on-chain backtesting. This model not only enhances the personalization and responsiveness of strategy generation but, more importantly, allows non-expert users to delegate their holdings using natural language for the first time, significantly lowering the technical barriers to asset management.

Another rapidly maturing use case is on-chain identity and social interaction. Traditional on-chain identity systems have largely relied on transaction histories, asset holdings, or specific proof mechanisms (such as POAP), with limited expressiveness and adaptability. With the help of AI models, users can now have "semantic agents" that continuously synchronize with their personal preferences, interests, and behaviors. These agents can participate in social DAOs, publish content, and organize NFT campaigns on behalf of users and even help them maintain their on-chain reputation and influence. For instance, some social blockchain projects have already started deploying agents that support MCP to automatically assist new users with onboarding, establishing social graphs, and participating in discussions and votes, thereby transforming the "cold start problem" from a product design issue to one concerning the participation of smart agents. In a future where identity diversity and personality bifurcation are widely embraced, a user may have multiple AI agents for different social scenarios. In this context, MCP would serve as the "identity governance layer" that manages these agents' behavior standards and execution permissions.

The third key application of AI agents lies in governance and DAO management. Today, DAOs continue to face bottlenecks in user engagement and governance participation, compounded by high technical barriers and behavioral noise in voting mechanisms. With the introduction of MCP, agents equipped with semantic interpretation and intent understanding capabilities can help users stay updated on DAO developments, extract key information, semantically summarize proposals, and recommend voting options or even vote automatically based on a clear understanding of user preferences. This on-chain governance model, which is based on a "preference agent" mechanism, considerably alleviates issues of information overload and misaligned incentives. At the same time, the MCP framework allows models to share governance experiences and strategy evolution paths. For example, if an agent observes negative externalities stemming from a certain type of governance proposal across multiple DAOs, it can feed that insight back into the model, creating a mechanism for the transfer of governance knowledge across communities. This contributes to the establishment of increasingly "intelligent" governance structures.

Apart from the above mainstream applications, MCP also offers a unified interface for scenarios such as on-chain data curation, interaction within game worlds, automated ZK proof generation, and cross-chain task relays. In the GameFi space, AI agents can serve as the brains behind non-player characters (NPCs), enabling real-time dialogue, plot generation, task scheduling, and behavioral evolution. In the NFT ecosystem, models can act as "semantic curators", dynamically recommending NFT collections based on user interests and even generating personalized content. Furthermore, in the ZK field, models can leverage structured compilation to swiftly translate intents into ZK-friendly constraint systems, streamlining the generation of zero-knowledge proofs and making development more accessible.

It is clear from the commonalities across these use cases that what MCP is transforming is not just the performance of individual features of any certain application, but the very paradigm of task execution. Traditional Web3 task execution presupposes that users "know what to do", requiring them to master underlying knowledge such as contract logic, transaction structures, and network fees. In contrast, MCP flips this paradigm to "users simply say what they want to do", and the models would take care of the rest. The interaction layer between users and the blockchain evolved from code-based interfaces to semantic interfaces and from function calls to intent orchestration. This fundamental shift elevates AI from a mere "tool" to a "behavioral agent" and transforms the blockchain from a "protocol network" into an "interactive context".

Chapter 4: MCP's Market Prospects and In-Depth Analysis of Its Industrial Applications

As a cutting-edge innovation for the convergence of AI and blockchain technologies, MCP not only introduces a brand new economic model to the crypto market but also unlocks novel development opportunities for multiple industries. With continued advancements in AI technology and the growing expansion of blockchain use cases, MCP is showing tremendous market potential. This chapter will offer an in-depth analysis of MCP's application prospects across various industries, delving into market dynamics, technological innovation, industry chain innovation, and more.

4.1 Market Potential of the AI + Crypto Convergence

The convergence of AI and blockchain has become a powerful driving force behind the global digital transformation. Especially driven MCP, AI models can not only perform tasks but also engage in on-chain value exchange, evolving into independent economies. As AI technology continues to advance, an increasing number of AI models are taking on real-world market tasks in areas such as product manufacturing, service delivery, and financial decision-making. In the meantime, blockchain's decentralization, transparency, and immutability provide these models with an ideal trust mechanism, accelerating their adoption and application across diverse industries.

The convergence of AI and the crypto market is expected to experience explosive growth in the coming years. As a pioneer of this trend, MCP is poised to play a central role, especially in fields such as finance, healthcare, manufacturing, smart contracts, and digital asset management. The rise of AI native assets is creating abundant opportunities for developers and investors while also driving unprecedented disruption across traditional industries.

4.2 Diversified Market Applications and Cross-Industry Collaboration

MCP is paving the way for cross-sector integration and collaboration for a variety of industries. Particularly in industries like finance, healthcare, and IoT, its applications will drive significant innovation and growth. In finance, MCP equips AI models with tradable assets featuring "revenue rights", fostering the evolution of the DeFi ecosystem. Beyond investing in AI models themselves, users can also trade the models' revenue rights on DeFi platforms via smart contracts. This model expands the range of investment options for investors and may incentivize more traditional financial institutions to venture into the blockchain and AI fields.

In healthcare, MCP supports AI applications in areas like precision medicine, drug R&D, and disease prediction. By analyzing large volumes of medical data, AI models can generate disease prediction models or identify promising paths for drug R&D, and collaborate with healthcare institutions through smart contracts. This collaboration not only enhances the efficiency of healthcare services but also provides transparent and equitable solutions for protecting data privacy and sharing the resulting benefits. MCP's incentive mechanisms ensure equitable distribution of benefits between AI models and healthcare providers, thereby encouraging the continued emergence of innovative technologies.

IoT applications, especially in the development of smart homes and smart cities, will also benefit from MCP. AI models can support IoT devices with intelligent decision-making by analyzing sensor data in real time. For example, AI can optimize energy consumption based on environmental data, improve coordination among devices, and lower overall system costs. MCP, in turn, offers reliable incentive and reward mechanisms for these AI models, motivating various participants and further accelerating IoT development.

4.3 Technological Innovation and Industry Chain Integration

MCP's market prospects lie not only in its technical breakthroughs but also in its ability to drive integration and collaboration across entire industry chains. By bridging blockchain and AI, MCP promotes deeper convergence across industry chains, breaking down traditional industrial barriers and enabling cross-industry resource integration. For instance, in areas such as shared training data and algorithm optimization, MCP provides a decentralized platform where all parties can share computational resources and training data without relying on traditional centralized institutions. With this decentralized transaction method, MCP helps eliminate data silos in traditional industries, facilitating data flow and sharing.

In addition, MCP will promote greater technological openness and transparency. Developers and users can customize and optimize AI models independently through blockchain-based smart contracts. MCP's decentralized feature allows innovators and developers to collaborate in an open ecosystem and share their technological achievements, which offers essential support for industry-wide technological progress and innovation. At the same time, the convergence of blockchain and AI continues to expand its range of applications. From finance to manufacturing and from healthcare to education, MCP offers vast potential across these sectors.

4.4 An Investment Perspective: The Future Capital Markets and Commercialization Potential

As MCP becomes widely adopted and matures, it continues to draw increasing attention from investors. Through decentralized reward mechanisms and the tokenization of models' revenue rights, MCP offers investors various ways to participate. Investors can directly purchase revenue rights of AI models and profit from their market performance. Furthermore, MCP's tokenomics also introduces new types of investable assets to capital markets. In the future digital asset market, MCP-based AI model assets may become key investment targets, drawing in funds from various investors, including venture capital firms, hedge funds, and individual investors.

The participation of capital markets will not only fuel the broader adoption of MCP but also accelerate its path to commercialization. Enterprises and developers can secure funding to further develop and optimize AI models by financing, or by selling or licensing the revenue rights of these models. In this process, capital flows will serve as a powerful engine propelling technological innovation, market adoption, and industrial expansion. Investors' confidence in MCP will directly impact its standing and commercial value in global markets.

Chapter 5: Conclusion and Outlook

MCP represents a key direction in the convergence of AI and the crypto market. It demonstrates enormous potential, particularly in areas such as DeFi, data privacy protection, smart contract automation, and the tokenization of AI assets. As AI technology matures, more industries will gradually incorporate AI capabilities, and MCP will offer these models a decentralized, transparent, and traceable operation platform. This framework not only enhances the efficiency and value of AI models but also facilitates their broader market adoption.

In the past few years, blockchain technology and AI have gradually converged from their separate fields. With ongoing technological development, their integration offers new solutions for various industries while contributing to the creation of innovative business models. It was against this backdrop that MCP emerged. By introducing decentralization and incentive mechanisms, it harnesses the complementary strengths of AI and blockchain to bring unprecedented innovation to the crypto market. As AI and blockchain technologies continue to mature, MCP will not only reshape the digital asset economy but also inject fresh momentum into global economic transformation.

From an investment viewpoint, MCP applications will attract substantial capital inflows, especially from venture capital firms and hedge funds pursuing innovative opportunities. As more AI models become tokenized, tradable, and capable of value appreciation through MCP, the resulting market demand will further boost the protocol's adoption. Moreover, MCP's decentralized nature reduces the risk of single points of failure common in centralized systems, thereby reinforcing its long-term stability in the global market.

As the MCP ecosystem continues to diversify in the future, AI and crypto assets built on the protocol may emerge as mainstream investment vehicles within digital currency and financial markets. These AI-based assets not only serve as value-appreciation tools in the crypto market but may also evolve into major financial commodities globally, contributing to a new international economic landscape.