MCP: The Core Infrastructure 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. Similar to other emerging concepts in the Web3 AI field, MCP( stands for Model Context Protocol), which originated from Web2 AI and is now being redefined in the Web3 environment.

Overview of MCP

MCP is an open protocol for standardizing the way applications communicate context information to large language models (LLMs). This enables more seamless collaboration between tools, data, and AI agents.

The importance of MCP ###

The main 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 these capability gaps, allowing AI Agents to utilize various tools.

MCP can be compared to a unified interface standard in the AI application field, making it easier for AI to connect with various data sources and functional modules. Imagine each LLM as a different device using different interfaces. If you are a hardware manufacturer, you would need to develop a set of accessories for each interface, resulting in extremely high maintenance costs.

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

This standardized protocol is beneficial for both parties:

• For AI Agent( client): It can safely connect to external tools and real-time data sources.
• For tool developers ( server ): one-time integration, cross-platform available

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 to prioritize AI. Each API has its own structure and documentation, and developers must manually specify parameters and read interface documentation. The AI Agent itself is unable to read documents and must be hard-coded to adapt to each type of API.

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.

The deployment of MCP has become much simpler. Developers can now deploy remote MCP servers directly on the cloud platform with minimum device configuration. This greatly simplifies the deployment and management process of MCP servers, including authentication and data transmission, making it a "one-click deployment."

Although the MCP itself may seem unremarkable, it is by no means insignificant. As a purely infrastructural component, the MCP cannot be used directly by consumers; its value only becomes truly apparent when higher-level AI agents call the MCP tools and demonstrate tangible results.

Web3 AI and MCP Ecosystem

AI in Web3 also faces the issues of "lack of contextual data" and "data islands", 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 connected, 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 to facilitate the seamless integration of third-party plugins and tools. As a result, a new generation of AI Agent infrastructure and applications based on the 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

There is a project developing a decentralized MCP Server marketplace, focusing on native encryption 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 functionality to lower the usage threshold.

Another project also offers the MCP Server registration system, focusing on the cryptocurrency field and further expanding to the A2A( Agent-to-Agent) protocol.

A2A is an open protocol designed to enable secure communication, collaboration, and task coordination between different AI agents (Agent). A2A supports enterprise-level AI collaboration, allowing AI agents from different companies to work together on tasks.

If MCP focuses on the interaction between the Agent( client) and the tool( server), then A2A is more like a collaboration middleware between Agents, allowing multiple Agents to work together to complete tasks without sharing internal states. They collaborate through context, instructions, state updates, and data transmission.

In short:

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

The Combination of MCP Server and Blockchain

The integration of blockchain technology in MCP Server has multiple benefits:

1. Obtain long-tail data through the native incentive mechanism of encryption, encouraging the community to contribute scarce datasets.

2. Defend against "tool poisoning" attacks, where malicious tools disguise themselves as legitimate plugins to mislead Agents.

   • Blockchain provides cryptographic verification mechanisms, such as TEE Remote Attestation, ZK-SNARK, FHE, etc.

3. Introduce a staking/punishment mechanism to build the trust system of the MCP server in conjunction with the 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 ESG data sources, enriching 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.

However, the current MCP project is still in its early stages. Most platforms are still centralized plugin markets, where project teams manually organize third-party Server tools from open-source platforms and develop some plugins in-house. Essentially, there is not much difference from the Web2 plugin market, the only difference being the focus on Web3 scenarios.

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. Some industry leaders are urging AI developers to actively build high-quality MCP Servers to provide a richer toolkit for AI Agents on specific blockchains.

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 become 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 economic system empowered by cryptographic technology and blockchain as a medium.

Of course, the MCP itself is not directly aimed at end users; it is a foundational protocol layer. In other words, the true value and potential of the 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 blockchain and encryption mechanisms build a trusted, efficient, and composable economic system for this intelligent network.