MCP: The New 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 (which stands for Model Context Protocol) originates from Web2 AI and is now being reimagined in the 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 allows for a 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 independently 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 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.

Imagine each LLM as a different mobile phone—each with its own type of interface. If you are a hardware manufacturer, you need to develop a set of accessories for each interface, leading to extremely high maintenance costs. This is precisely the challenge faced by AI tool developers: customizing plugins for each LLM platform greatly increases complexity and limits scalability. MCP is designed to address this issue by establishing a unified standard.

This standardized protocol benefits both parties:

• For AI Agent (client): external tools and real-time data sources can be safely integrated.
• For tool developers (server side): one integration, available 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 the API is meant to serve humans, not prioritize AI. Each API has its own structure and documentation, and developers must manually specify parameters and read the interface documentation. However, 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 internal API, providing Agents with a unified calling method. MCP can be viewed as an API adaptation layer encapsulated for Autonomous Agents.

Recently, a certain cloud service platform announced that developers can directly deploy remote MCP servers on its platform with minimum device configuration. This greatly simplifies the deployment and management process of MCP servers, including authentication and data transmission, and can be called "one-click deployment."

Although MCP itself may seem unappealing, it is by no means insignificant. As a purely infrastructural 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 results.

Web3 AI and MCP Ecosystem

AI in Web3 also faces the issues of "lack of contextual data" and "data silos". This means that AI cannot access real-time data on the chain or natively execute smart contract logic.

In the past, some projects attempted to build multi-Agent collaborative networks, but ultimately fell into the dilemma of "reinventing the wheel" 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.

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 interact natively with DeFi protocols.

Project Case: DeMCP and DeepCore

DeMCP is a decentralized MCP Server marketplace that focuses on native crypto 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 encryption field and further expanding into 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 tools access capabilities for Agents
• A2A: Provides agents with the ability to collaborate with each other.

Why do MCP servers need blockchain?

The MCP Server integrates blockchain technology with 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 the Agent.
   • Blockchain provides cryptographic verification mechanisms such as TEE Remote Attestation, ZK-SNARK, FHE, etc.
3. Introduce a staking/penalty mechanism and combine it with an on-chain reputation system to build the trust system of the MCP server.
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, 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 MCP project is still in the early stages. Most platforms are still centralized plugin markets, where project teams manually sort third-party server tools from GitHub and develop some plugins in-house. Essentially, there is not much difference from the Web2 plugin market, the only distinction being the focus on Web3 scenarios.

Future Trends and Industry Impact

Currently, more and more people in the crypto industry are beginning to realize the potential of MCP in connecting AI and blockchain. For example, the founder of a well-known trading platform recently publicly called on AI developers to actively build high-quality MCP Servers to provide a richer toolkit for AI Agents on their chain.

As 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 be an MCP server.

This may give rise to a new pricing mechanism: Agents can dynamically choose tools based on execution speed, cost efficiency, relevance, etc., forming a more efficient Agent service economic system empowered by Crypto and blockchain as a medium.

Of course, MCP itself is not directly aimed at end users; it is a foundational protocol layer. In other words, 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 trustworthy, efficient, and composable economic system for this intelligent network.