MCP: The New Cornerstone of the Web3 AI Agent Ecosystem

MCP is becoming a key component in the Web3 AI Agent ecosystem. It introduces the MCP Server through a plugin-like architecture, providing new tools and capabilities for AI Agents. MCP stands for Model Context Protocol, originally from the Web2 AI field, and is now being redefined for Web3 scenarios.

Overview of MCP

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

The importance of MCP ###

The main limitations faced by current large language models include:

• Unable to browse the web in real time
• Cannot directly access local or private files
• Unable to interact autonomously 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 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 addresses the complexity issue that AI tool developers face when customizing plugins for each LLM platform, facilitating scalable expansion.

The standardized protocol of MCP benefits both AI agents and tool developers:

• AI Agent( client) can securely access external tools and real-time data sources.
• Tool developers can connect once to the server ( and use it across platforms.

This will ultimately form a more open, interoperable, and low-friction AI ecosystem.

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) The difference between MCP and traditional APIs

API design is primarily human-oriented, rather than AI-first. Each API has a unique structure and documentation, and developers need to manually specify parameters and read the interface documentation. AI agents cannot directly read documentation and must hard-code adaptations for each API.

MCP standardizes the function call format within the API, abstracts the unstructured parts, and provides a unified calling method for Agents. It can be seen as the API adaptation layer for Autonomous Agents.

Recently, Cloudflare announced that developers can directly deploy remote MCP servers on its Workers platform, greatly simplifying the deployment and management process.

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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.

In the past, some projects attempted to build multi-agent collaborative networks but fell into difficulties due to reliance on centralized APIs and custom integrations. To address this bottleneck, the next generation of AI agents needs a more modular, Lego-like architecture to seamlessly integrate third-party plugins and tools.

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

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) Project Case

DeMCP is a decentralized MCP Server marketplace, focusing on native encryption tools and ensuring the sovereignty of MCP tools. Its features include:

• Use TEE to ensure that the MCP tool has not been tampered with.
• The token incentive mechanism encourages developers to contribute.
• Provide MCP aggregator and micropayment functionality

DeepCore provides an MCP Server registration system, focusing on the encryption field, and expanding to A2A### Agent-to-Agent( protocol.

A2A is an open protocol proposed by Google, aimed at achieving secure communication, collaboration, and task coordination between different AI agents. It supports enterprise-level AI collaboration, such as AI agents from different companies working together to handle tasks.

In brief:

• 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 MCP Server integrates blockchain technology with various advantages:

1. Obtain long-tail data through the encrypted native incentive mechanism.
2. Defending against "tool poisoning" attacks
3. Introduce a staking/punishment mechanism to build a trust system in conjunction with an on-chain reputation system.
4. Improve system fault tolerance and real-time performance
5. Promote Open Source Innovation

In the future, AI Agents will be able to autonomously search for the necessary MCP tools to complete complex tasks. Currently, the MCP project is still in its early stages, and most platforms are still centralized plugin markets.

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Future Trends and Industry Impact

People in the cryptocurrency industry are beginning to recognize the potential of MCP in connecting AI and blockchain. As infrastructure matures, companies' competitive advantages will shift towards providing a richer, more diverse, and easily combinable toolkit.

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

As a bottom layer protocol, the true value and potential of MCP will be revealed when integrated with AI Agents and transformed into practical applications. Ultimately, Agents serve as carriers and amplifiers of MCP's capabilities, while blockchain and encryption mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.

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