MCP: The Key Technology for Advancing the Next Generation of Web3 AI Agents

MCP (Model Context Protocol) 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. MCP originated in the Web2 AI field and is now being reimagined and applied in the Web3 environment.

MCP Overview

MCP is an open protocol aimed at standardizing the way applications convey contextual information to large language models (LLMs). This allows for more seamless collaboration between tools, data, and AI Agents.

The importance of ### MCP

Current large language models face several core limitations:

• Unable to browse the internet in real-time
• Unable to directly access local or private files
• Unable to interact independently with external software

MCP serves as a universal interface layer, filling 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 AI Agents (clients) and tool developers (servers):

• AI Agent can securely connect to external tools and real-time data sources.
• Tool developers can integrate once and use across platforms.

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

The difference between MCP and traditional APIs

The design of APIs is primarily aimed at humans rather than being 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 the documentation and must be hard-coded to adapt to each API.

MCP abstracts these unstructured parts by standardizing the function call format within the API, providing a unified calling method for Agents. MCP can be seen as an API adaptation layer encapsulated for autonomous Agents.

Web3 AI and MCP Ecosystem

AI in Web3 also faces the issues of "lack of contextual data" and "data silos." 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 MCP and A2A protocols is emerging, designed specifically for the Web3 scenario, allowing Agents to access multi-chain data and natively interact with DeFi protocols.

Project Case

DeMCP

DeMCP is a marketplace for decentralized MCP Servers, focusing on native cryptographic tools and ensuring the sovereignty of MCP tools. Its advantages include:

• Use Trusted Execution Environment (TEE) to ensure the MCP tool is not tampered with
• Use token incentive mechanisms to encourage developers to contribute to the MCP server.
• Provide MCP aggregator and micropayment functions to lower the usage threshold.

DeepCore

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

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

In short:

• MCP: Provides Agent with tool access capabilities
• A2A: Providing agents with the ability to collaborate with each other

The Combination of MCP Servers and Blockchain

The MCP Server integrates blockchain technology, which has multiple benefits:

1. Obtain long-tail data through a native encryption incentive mechanism, encouraging the community to contribute scarce datasets.
2. Defend against "tool poisoning" attacks, where malicious tools disguise themselves as legitimate plugins to mislead Agents.
3. Introduce staking/penalty mechanisms and build a trust system for the MCP server in conjunction with the on-chain reputation system.
4. Improve system 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 sources like ESG data, enriching ecological diversity.

Future Trends and Industry Impact

With the maturity of infrastructure, the competitive advantage of "developer-first" companies will shift from API design to providing a richer, more diverse, and easily combinable toolkit.

In the future, every application could become an MCP client, and every API could be an MCP server. This may give rise to new pricing mechanisms: Agents could dynamically select tools based on execution speed, cost efficiency, relevance, etc., forming a more efficient Agent service economy empowered by cryptocurrency and blockchain.

The true value and potential of MCP can only be truly seen when AI Agents integrate it and transform it into practical applications. Ultimately, Agents are the carriers and amplifiers of MCP's capabilities, while blockchain and cryptographic mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.