MCP: The Core Engine 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 (short for Model Context Protocol) originates from Web2 AI and is now being reimagined in a Web3 environment.

The Essence of MC

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

The importance of MCP ###

Currently, large language models face several major limitations:

• Unable to browse the web in real-time
• Unable to directly access local or private files
• Unable to interact independently with external software

MCP serves 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. Imagine each LLM is a different device using different interfaces. If you are a developer, you need to create a set of accessories for each interface, which results 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 scalable expansion. MCP was created to solve this problem by establishing a unified standard, allowing all LLMs and tool vendors to use the same interface.

This standardized protocol is beneficial for both parties:

• For AI Agent (Client): Can securely connect to external tools and real-time data sources.
• For tool developers (server-side): one integration, cross-platform available

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

The 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 hard-coded to adapt to each type of API (such as REST, GraphQL, RPC, etc.).

MCP abstracts these unstructured parts by standardizing the function call format of the API, providing a unified calling method for Agents. MCP can be seen as an API adaptation layer that encapsulates the Autonomous Agent.

Recently, a well-known cloud service platform announced that developers can directly deploy remote MCP servers on its platform with minimal device configuration. This greatly simplifies the deployment and management process of MCP servers, including authentication and data transfer, and can be described as "one-click deployment."

Although the MCP itself may seem unappealing, it is by no means insignificant. As a purely infrastructural component, the MCP cannot be used directly by consumers; its value will only truly be revealed when upper-layer AI agents call upon the MCP tools and demonstrate actual results.

Web3 AI and 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 onboarded, 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.

Thus, 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 natively interact with DeFi protocols.

Project Case: DeMCP and DeepCore

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

Another project, DeepCore, also provides an MCP Server registration system, focusing on the cryptocurrency sector and further expanding into 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: Provide agents with the ability to collaborate with each other.

The Value of Blockchain to MCP Servers

The MCP Server integrates blockchain technology, offering 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/punishment mechanism, and 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, avoiding 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 parsing user natural language prompts. In the future, AI Agents will be able to autonomously search for the necessary MCP tools to accomplish complex task objectives.

However, the 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 GitHub and develop some plugins themselves. Essentially, this is not much different from the Web2 plugin market, with the only difference being the focus on Web3 scenarios.

Future Trends and Industry Impact

More and more people in the cryptocurrency industry are beginning to realize the potential of MCP in bridging 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 could potentially become an MCP client, and every API could be an MCP server. This could give rise to a new pricing mechanism: Agents could dynamically select tools based on execution speed, cost efficiency, relevance, etc., forming a more efficient Agent service economic system empowered by cryptocurrency 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 truly 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.