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(, full name Model Context Protocol), originates from Web2 AI and is now being redefined in the Web3 environment.

Introduction to MCP

MCP is an open protocol designed to standardize 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 ###

The main limitations facing 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 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 Agent( clients) and tool developers( servers):

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

The final 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 accommodate each API.

MCP abstracts these unstructured parts by standardizing the function call format of the API internally, 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 real-time on-chain 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. To overcome this bottleneck, the next generation of AI agents needs a more modular, Lego-like architecture to facilitate seamless integration of third-party plugins and tools.

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

Project Case

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

• Use the TEE( Trusted Execution Environment ) to ensure that the MCP tool has not been 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 also provides an MCP Server registration system, focusing on the cryptocurrency field and further expanding to 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, such as allowing AI agents from different companies to work together on tasks.

In brief:

• MCP: Provides tools access capabilities for Agents
• A2A: Provide agents with the ability to collaborate with each other.

MCP Server and Blockchain

The integration of blockchain technology in MCP Server has many 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 to build the trust system of 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 ESG data sources and enrich ecological diversity.

Currently, most MCP Server infrastructure still matches tools by parsing user natural language prompts. In the future, AI Agents will be able to autonomously search for the required MCP tools to accomplish complex task objectives.

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 also 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 potentially be an MCP server. This may 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 cryptographic technology and blockchain as a medium.

MCP itself is a layer of underlying protocol, 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's capabilities, while the blockchain and encryption mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.