MCP: The Core Engine 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. MCP stands for Model Context Protocol, originating from Web2 AI, and is now being reimagined in the Web3 environment.

Introduction to MCP

MCP is an open protocol designed to standardize the way applications communicate contextual information to large language models (LLMs). This enables more seamless collaboration between tools, data, and AI Agents.

The importance of ### MC

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 with external software autonomously

MCP fills the capability gap mentioned above by acting as a universal interface layer, enabling AI Agents to use 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. Imagine each LLM as a different device; if you are a hardware manufacturer, you would need to develop a set of accessories for each interface, resulting in very high maintenance costs.

This is precisely the problem faced by AI tool developers: customizing plugins for each LLM platform greatly increases complexity and limits scalability. MCP is designed to solve this issue by establishing a unified standard.

This standardized protocol is beneficial for both parties:

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

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

Although MCP itself may seem unappealing, it is not insignificant. As a pure infrastructure component, MCP cannot be used directly by consumers; its value will only truly manifest when upper-layer AI agents invoke MCP tools and demonstrate actual effects.

Web3 AI x MCP Ecological Diagram

AI in Web3 also faces the issues of "lack of contextual data" and "data islands", where 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 for seamless integration of third-party plugins and tools. As a result, 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: DeMCP and DeepCore

DeMCP is a decentralized marketplace for MCP Servers, focusing on native cryptographic 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 token incentive mechanisms to encourage developers to contribute to the MCP server.
• Provide MCP aggregator and micropayment features to lower the usage threshold.

DeepCore also provides an MCP Server registration system, focusing on the cryptocurrency field, 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, such as allowing AI agents from different companies to work together on tasks.

In short:

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

The Combination of MCP Server 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/punishment mechanism, combined with an on-chain reputation system to build the trust system of the MCP server.
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.

Currently, most MCP Server infrastructure still matches tools by interpreting 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 professionals in the crypto industry are beginning to realize the potential of MCP in connecting AI and blockchain. As infrastructure matures, the competitive advantage of "developer-first" companies will shift from API design to: who can provide a richer, more diverse, and easily combinable toolkit.

In the future, every application may become an MCP client, and every API may serve as 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.

MCP itself does not directly target end users; it is a foundational protocol layer. The true value and potential of MCP can only be realized 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.