MCP: The New Core of the Web3 AI Agent Ecosystem

MCP is rapidly becoming a core component of the Web3 AI Agent ecosystem. It introduces the MCP Server through a plugin-like architecture, providing new tools and capabilities for AI Agents. As one of the emerging narratives in the Web3 AI space, MCP(, which stands for Model Context Protocol), originates from Web2 AI and is now being reimagined in the Web3 environment.

The Essence and Importance of MCP

MCP is an open protocol designed to standardize the way applications convey contextual information to large language models (LLMs). It enables more seamless collaboration between tools, data, and AI Agents.

The main limitations faced by current large language models include:

• Unable to browse the internet in real time
• Unable to directly access local or private files
• Cannot autonomously interact with external software

MCP serves as a universal interface layer, bridging these capability gaps and enabling AI Agents to utilize various tools. MCP can be likened to a unified interface standard in the field of AI applications, making it easier for AI to connect with various data sources and functional modules.

This standardized protocol is beneficial for both parties:

• For AI Agent( client): can safely connect to external tools and real-time data sources.
• Service for tool developers ( server ): one-time integration, cross-platform available

The end result is a more open, interoperable, and low-friction AI ecosystem.

Differences Between MCP and Traditional APIs

The design of the API is meant to serve humans, not AI-first. Each API has its own structure and documentation, and developers must manually specify parameters and read the interface documentation. The AI Agent itself cannot read documentation and must be hard-coded to adapt to each API such as REST, GraphQL, RPC, etc.(.

MCP standardizes the function call format within the API, abstracting these unstructured parts to provide a unified calling method for Agents. MCP can be viewed as an API adaptation layer encapsulated for Autonomous Agents.

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Web3 AI and MCP Ecosystem

AI in Web3 also faces the issues of "lack of contextual data" and "data silos", meaning that AI cannot access on-chain real-time data or natively execute smart contract logic.

A new generation of AI Agent infrastructure and applications based on the MCP and A2A protocols is emerging, specifically designed for Web3 scenarios, allowing Agents to access multi-chain data and interact natively with DeFi protocols.

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Project Cases

Some projects are exploring the application of MCP in the Web3 field:

A certain project is a marketplace for a decentralized MCP Server, focusing on native encryption tools and ensuring the sovereignty of MCP tools. Its advantages include:

• Use the TEE) trusted execution environment ( to ensure that the MCP tool has not been tampered with.
• Use a token incentive mechanism to encourage developers to contribute to the MCP server.
• Provide MCP aggregator and micropayment functions to lower the usage threshold.

Another project also provides the MCP Server registration system, focusing on the cryptocurrency field, and further expands to another open standard proposed by Google: A2A) Agent-to-Agent ( protocol.

A2A is an open protocol designed to enable secure communication, collaboration, and task coordination between different AI agents )Agent(. A2A supports enterprise-level AI collaboration, such as allowing AI agents from different companies to work together on tasks.

In short:

• MCP: Provides tools access capabilities for Agents
• A2A: Provides agents with the ability to collaborate with each other.

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MCP Server and Blockchain

The integration of blockchain technology in MCP Server has multiple benefits:

1. Acquire long-tail data through the native incentive mechanism of encryption, encouraging the community to contribute scarce datasets.

2. Defend against "tool poisoning" attacks, where malicious tools disguise themselves as legitimate plugins to mislead the Agent.

   • Blockchain provides encryption verification mechanisms, such as TEE Remote Attestation, ZK-SNARK, FHE, etc.

3. Introduce a staking/punishment mechanism and build a trust system for the MCP server in conjunction with the on-chain reputation system.

4. Enhance the system's fault tolerance and real-time performance to avoid single points of failure in centralized systems.

5. Promote open-source innovation, allowing small developers to publish ESG data sources, enriching ecological diversity.

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Future Trends and Industry Impact

As the infrastructure matures, the competitive advantage of "developer-first" companies will also shift from API design to: who can provide a richer, more diverse, and easily combinable toolkit.

In the future, every application may become an MCP client, and every API may become an MCP server. This could give rise to new pricing mechanisms: Agents can dynamically choose tools based on execution speed, cost efficiency, relevance, and so on, forming a more efficient Agent service economic system empowered by Crypto and blockchain as a medium.

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MCP itself does not directly target end users; it is a foundational protocol layer. The true value and potential of MCP can only be truly seen when AI Agents integrate it and transform it into practical applications.

Ultimately, the Agent is the carrier and amplifier of MCP capabilities, while blockchain and encryption mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.

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