MCP: The New Engine of the Web3 AI Agent Ecosystem

MCP is 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 space, MCP (short for Model Context Protocol) originates from Web2 AI and is now being reimagined in the Web3 environment.

The Essence and Importance of MC

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
• Unable to interact autonomously with external software.

MCP acts 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 different data sources and functional modules.

This standardized protocol is beneficial for both AI Agents (clients) and tool developers (servers):

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

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

Differences Between MCP and Traditional APIs

Traditional APIs are primarily designed for 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 documentation and must be hard-coded to adapt to each API.

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

The Ecological Landscape of Web3 AI and MCP

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

Project Case

1. DeMCP: A marketplace for a decentralized MCP Server, focusing on native crypto 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 reduce the barrier to use.

2. DeepCore: Provides an MCP Server registration system, focusing on the cryptocurrency field, and further expanding into the A2A (Agent-to-Agent) protocol proposed by Google.

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 brief:

• MCP: Provides agents with tool access capabilities
• A2A: Provides agents with the capability to collaborate with each other.

The Combination of MCP Server and Blockchain

The MCP Server integrates blockchain technology with various 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 the Agent.
3. Introduce a staking/penalty mechanism and establish a trust system for the MCP server in conjunction with an on-chain reputation system.
4. Enhance 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 such as ESG data, enriching ecological diversity.

Future Trends and Industry Impact

More and more people in the cryptocurrency industry are beginning to realize the potential of MCP in connecting AI and blockchain. As the 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 as a medium.

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 serves as the carrier and amplifier of MCP's capabilities, while the blockchain and encryption mechanisms construct a trustworthy, efficient, and composable economic system for this intelligent network.