MCP: The Core Infrastructure of the Web3 AI Agent Ecosystem

MCP is rapidly becoming a key 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) originated from Web2 AI and is now being redefined 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). This enables a more seamless collaboration between tools, data, and AI Agents.

The core limitations faced by current large language models include:

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

MCP acts as a universal interface layer, bridging the aforementioned capability gaps, allowing 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.
• For tool developers (server-side): One integration, cross-platform available

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

Differences between MCP and Traditional APIs

The design of APIs is intended 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 abstracts these unstructured parts by standardizing the function call format within the API, providing a unified calling method for Agents. MCP can be viewed as an API adaptation layer encapsulated for Autonomous Agents.

Web3 AI x MCP Ecosystem Diagram

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

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.

Project Case: DeMCP and DeepCore

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

• Use 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.

DeepCore also 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. A2A supports enterprise-level AI collaboration, 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.

The Combination of MCP Servers and Blockchain

The MCP Server integrates blockchain technology with multiple benefits:

1. Obtain long-tail data through an encrypted native 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 the Agent.
3. Introduce a staking/penalty mechanism to construct the trust system of the MCP server in conjunction with the on-chain reputation system.
4. Enhance system fault tolerance and real-time capabilities to avoid single points of failure in centralized systems.
5. Promote open source innovation, allowing small developers to publish data sources such as ESG, enriching ecological diversity.

Future Trends and Industry Impact

As 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 could become an MCP client, and every API could be an MCP server. This could give rise to new pricing mechanisms: Agents can dynamically select tools based on execution speed, cost efficiency, relevance, etc., forming a more efficient Agent service economy empowered by Crypto and blockchain as a medium.

Of course, MCP itself is not directly aimed at 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 the blockchain and encryption mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.