MCP: The Emerging Core 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 space, MCP (Model Context Protocol) originated from Web2 AI and is now being reimagined in a Web3 environment.

Definition and Importance of MCP

MCP is an open protocol for standardizing the way applications pass contextual information to large language models (LLMs). This allows tools, data, and AI Agents to collaborate more seamlessly.

The core limitations currently faced by 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, filling these capability gaps and enabling AI Agents to utilize various tools. MCP can be likened to USB-C in the field of AI applications—a unified interface standard that makes 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, available across platforms

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

Differences between MCP and traditional APIs

The design of APIs is meant 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 hardcoded 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 seen as an API adaptation layer encapsulated for the Autonomous Agent.

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 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. 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 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 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 Cases

DeMCP

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 token incentive mechanisms to encourage developers to contribute to the MCP server.
• Provide MCP aggregator and micropayment functions to lower the usage threshold.

DeepCore

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, allowing AI agents from different companies to work together on tasks.

In short:

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

The Combination of MCP Servers and Blockchain

The MCP Server integrates blockchain technology, which 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 Agents.
3. Introduce a staking/punishment mechanism, combined with the on-chain reputation system to build the trust system of the MCP server.
4. Enhance the system's 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 resources such as ESG data sources, 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 also 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 become an MCP server. This could give rise to new pricing mechanisms: Agents can dynamically select tools based on execution speed, cost efficiency, relevance, and other factors, forming a more efficient Agent service economy powered by Crypto and blockchain as mediators.

MCP itself does not directly target end users; it is a lower-level 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 trusted, efficient, and composable economic system for this intelligent network.