Will Google's MCP Protocol Become the Golden Communication Standard for Web3 AI Agent Development?
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Will Google's MCP Protocol Become the Golden Communication Standard for Web3 AI Agent Development?
The intuitive feeling is "culture shock."
Navigating Web3 tides with focused insightsBy Haotian
If Google's A2A and Anthropic's MCP protocol were to become the golden communication standard for web3 AI Agents, what would happen? The immediate intuition is "incompatibility." In my view, the environment faced by web3 AI Agents differs significantly from that of web2 ecosystems, and the challenges confronting core communication protocols are fundamentally distinct:
1) Application Maturity Gap: A2A and MCP were able to rapidly gain traction in web2 because they serve sufficiently mature application scenarios—essentially acting as "value amplifiers" rather than value creators. In contrast, most web3 AI Agents remain at a rudimentary stage of one-click Agent deployment, lacking deep application use cases (such as DeFAI, GameFAI, etc.), making it difficult for these protocols to be directly reused or deliver value.
For example, when a user writes code in Cursor, they can use the MCP protocol as a connector to push code updates directly to GitHub without leaving their current workflow—the MCP protocol adds convenient finishing touches. But if a user attempts to execute on-chain transactions using locally fine-tuned models in a web3 setting, they may become completely lost when trying to reach out, parse, and analyze on-chain data.
2) Critical Infrastructure Deficit: To build a complete ecosystem, web3 AI Agents must first fill severe gaps in foundational infrastructure—including unified data layers, Oracle layers, intent execution layers, decentralized consensus layers, and more. While in web2 environments A2A allows Agents to easily invoke standardized APIs for functional collaboration, even a simple cross-DEX arbitrage operation presents enormous challenges in web3.
Consider this scenario: a user instructs an AI Agent to "buy ETH on Uniswap when the price drops below $1,600, then sell when the price recovers." Despite appearing straightforward, the Agent must simultaneously address a series of web3-specific issues such as real-time parsing of on-chain data, dynamic Gas fee optimization, slippage control, and MEV protection. Meanwhile, a web2 AI Agent could accomplish similar tasks simply by calling standardized APIs. The disparity in infrastructure maturity between web2 and web3 is nothing short of astronomical.
3) Unique Requirements for Web3 AI Differentiation: If web3 AI Agents merely copy web2 protocols and functional patterns, they will struggle to leverage the unique characteristics of on-chain transaction ecosystems—especially complex issues like data noise, transaction accuracy, and diverse Router architectures.
Take intent-based transactions as an example. In a web2 context, when a user says "book the cheapest flight," the A2A protocol enables multiple Agents to collaborate effortlessly. But in web3, when a user wants to "bridge my USDC to Solana at the lowest cost and participate in liquidity mining," the Agent must not only understand the user’s intent but also balance security, atomicity, and cost efficiency while executing a series of intricate operations on-chain. In other words, if a seemingly convenient experience exposes users to greater security risks, then such convenience is meaningless—and the demand itself is illusory.
That's all.
In summary, I want to emphasize: the value of A2A and MCP is undeniable, but we cannot expect them to seamlessly fit into the web3 AI Agent landscape without significant adaptation. Isn't this very gap in missing infrastructure precisely the opportunity for builders?
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Haotian | CryptoInsight
