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MCP: The Infrastructure Revolution of Web3 AI Agent Ecosystem
MCP: The Core Infrastructure 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. Similar to other emerging concepts in the Web3 AI field, MCP (Model Context Protocol) originates from Web2 AI and is now being reimagined in the Web3 context.
MCP Overview
MCP is an open protocol designed to standardize the way applications convey contextual information to large language models (LLMs). This enables tools, data, and AI Agents to collaborate more seamlessly.
The importance of MCP ###
The main limitations faced by current large language models include:
MCP acts as a universal interface layer, filling these capability gaps and enabling AI Agents to utilize various tools.
MCP can be compared 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 and tool developers:
The final result is a more open, interoperable, and low-friction AI ecosystem.
The difference between MCP and traditional APIs
The design of traditional APIs is meant for human use, 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 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," meaning that AI cannot access on-chain real-time data or natively execute smart contract logic.
In the past, some projects attempted to build multi-agent collaborative networks, but ultimately fell into the "reinventing the wheel" dilemma due to reliance on centralized APIs and custom integrations. Each time a data source was integrated, the adaptation layer had to be rewritten, leading to skyrocketing development costs.
To address this bottleneck, the next generation of AI Agents requires a more modular, Lego-like architecture to facilitate the seamless integration of third-party plugins and tools. The new infrastructure and applications for AI Agents based on MCP and A2A protocols are emerging, specifically designed for Web3 scenarios, allowing Agents to access multi-chain data and natively interact with DeFi protocols.
Project Case
DeMCP
DeMCP is a decentralized marketplace for MCP Servers, focusing on native cryptographic tools and ensuring the sovereignty of MCP tools. Its advantages include:
DeepCore
DeepCore provides an MCP Server registration system, focusing on the cryptocurrency field, and further expanding into 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:
The Combination of MCP Servers and Blockchain
The MCP Server integrates blockchain technology with various benefits:
Currently, most MCP Server infrastructure still matches tools by parsing user natural language prompts. In the future, AI Agents will be able to independently search for the required MCP tools to accomplish complex task objectives.
Future Trends and Industry Impact
As infrastructure matures, 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 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, etc., forming a more efficient Agent service economy empowered by cryptocurrency and blockchain.
MCP itself is a foundational protocol layer, and its true value and potential 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 cryptographic mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.