MCP: The Core Engine Powering the Next-Generation Web3 AI Agents
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MCP: The Core Engine Powering the Next-Generation Web3 AI Agents
The true value and potential of MCP can only be fully realized when AI Agents integrate it and transform it into practical applications.
Navigating Web3 tides with focused insightsBy: Frank Fu @IOSG
MCP is rapidly becoming central to the Web3 AI Agent ecosystem, introducing new tools and capabilities to AI Agents through a plugin-like architecture via MCP Servers.
Similar to other emerging narratives in the Web3 AI space (e.g., vibe coding), MCP—short for Model Context Protocol—originated in Web2 AI and is now being reimagined within the Web3 context.
What is MCP?
MCP is an open protocol introduced by Anthropic that standardizes how applications provide context information to large language models (LLMs), enabling seamless collaboration between tools, data, and AI Agents.
Why does it matter?
Key limitations of current LLMs include:
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Inability to browse the internet in real time
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No direct access to local or private files
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Inability to autonomously interact with external software
MCP bridges these capability gaps by acting as a universal interface layer, enabling AI Agents to use various tools.
You can think of MCP as the USB-C of the AI application world—a unified interface standard that makes it easier for AI to connect with diverse data sources and functional modules.
Imagine each LLM as a different phone—Claude uses USB-A, ChatGPT uses USB-C, and Gemini uses Lightning. As a hardware manufacturer, you’d need to develop separate accessories for each, leading to extremely high maintenance costs.
This is exactly the problem AI tool developers face: customizing plugins for every LLM platform increases complexity and limits scalability. MCP solves this by establishing a unified standard—like making all LLMs and tool providers use USB-C.
This standardized protocol benefits both sides:
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For AI Agents (clients): secure access to external tools and real-time data sources
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For tool developers (servers): one integration, cross-platform availability
The result is a more open, interoperable, and low-friction AI ecosystem.
How is MCP different from traditional APIs?
APIs are designed for humans, not AI-first. Each API has its own structure and documentation, requiring developers to manually specify parameters and read interface docs. AI Agents themselves cannot read documentation and must be hard-coded to adapt to each API type (e.g., REST, GraphQL, RPC).
MCP abstracts away these unstructured parts by standardizing internal function call formats within APIs, providing Agents with a uniform way to invoke tools. Think of MCP as an API adaptation layer specifically built for Autonomous Agents.
When Anthropic first launched MCP in November 2024, developers had to deploy MCP servers locally. But this May, Cloudflare announced during its Developer Week that developers could now deploy remote MCP servers directly on Cloudflare Workers with minimal setup—greatly simplifying deployment, management, authentication, and data transmission, effectively enabling "one-click deployment."
While MCP itself may seem unexciting at first glance, it is far from insignificant. As a pure infrastructure component, MCP cannot be used directly by end users. Its value only becomes evident when higher-level AI agents integrate MCP tools and demonstrate tangible functionality.
Web3 AI x MCP Ecosystem Landscape
AI in Web3 also faces challenges of "lack of contextual data" and "data silos," meaning AI cannot access real-time on-chain data or natively execute smart contract logic.
In the past, projects like ai16Z, ARC, Swarms, and Myshell attempted to build multi-agent collaborative networks but ultimately fell into the trap of "reinventing the wheel" due to reliance on centralized APIs and custom integrations.
Each new data source required rewriting the adapter layer, causing development costs to skyrocket. To overcome this bottleneck, next-generation AI Agents require a more modular, Lego-like architecture that enables seamless integration of third-party plugins and tools.
Thus, a new wave of AI Agent infrastructure and applications based on MCP and A2A protocols is emerging, designed specifically for Web3 scenarios, allowing Agents to access multi-chain data and natively interact with DeFi protocols.
▲ Source: IOSG Ventures
(This diagram does not fully cover all MCP-related Web3 projects)
Project Examples: DeMCP and DeepCore
DeMCP is a marketplace for decentralized MCP Servers (https://github.com/modelcontextprotocol/servers), focusing on native crypto tools and ensuring sovereignty over MCP tools.
Its advantages include:
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Using TEE (Trusted Execution Environment) to ensure MCP tools are untampered
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Token incentive mechanisms to encourage developer contributions to MCP servers
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Providing an MCP aggregator and micro-payment functionality to lower entry barriers
Another project, DeepCore (deepcore.top), also offers an MCP Server registration system focused on the crypto domain, further extending into another open standard proposed by Google: the A2A (Agent-to-Agent) protocol (https://x.com/i/trending/1910001585008058782).
A2A is an open protocol announced by Google on April 9, 2025, aimed at enabling secure communication, collaboration, and task coordination between different AI Agents. A2A supports enterprise-grade AI collaboration—for example, allowing a CRM agent from Salesforce to work with a Jira agent from Atlassian.
If MCP focuses on interaction between Agents (clients) and tools (servers), then A2A acts more like a collaboration middleware among Agents, enabling multiple Agents to cooperate on tasks without sharing internal states. They collaborate through context, instructions, status updates, and data exchange.
A2A is seen as the "common language" for AI Agent collaboration, driving cross-platform and cross-cloud AI interoperability, potentially transforming how enterprise AI operates. In short, A2A is like Slack for Agents—one Agent initiates a task, another executes it.
In summary:
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MCP: gives Agents access to tools
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A2A: enables Agents to collaborate with each other
Why do MCP Servers need blockchain?
Integrating blockchain technology into MCP Servers brings multiple benefits:
1. Leverage crypto-native incentives to acquire long-tail data and encourage community contributions of scarce datasets
2. Defend against "tool poisoning" attacks, where malicious tools impersonate legitimate plugins to mislead Agents
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Blockchain provides cryptographic verification mechanisms such as TEE Remote Attestation, ZK-SNARK, FHE, etc.
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See this article for details (https://ybbcapital.substack.com/p/from-suis-sub-second-mpc-network?utm_source=substack&utm_medium=email)
3. Introduce staking/punishment mechanisms combined with on-chain reputation systems to build trust in MCP Servers
4. Improve system fault tolerance and real-time performance, avoiding single points of failure in centralized systems like Equifax
5. Promote open-source innovation, enabling small developers to publish niche data sources such as ESG data, enriching ecosystem diversity
Currently, most MCP Server infrastructures still rely on parsing user natural language prompts for tool matching. In the future, AI Agents will be able to autonomously search for needed MCP tools to achieve complex task goals.
However, MCP projects are still in early stages. Most platforms remain centralized plugin markets, where teams manually curate third-party server tools from GitHub and develop some plugins in-house—essentially no different from Web2 plugin markets, except focused on Web3 use cases.
Future Trends and Industry Impact
Now, more people in the crypto industry are recognizing MCP’s potential in bridging AI and blockchain. For instance, Binance founder CZ recently publicly called on AI developers to actively build high-quality MCP Servers, providing richer toolsets for AI Agents on BNB Chain. The list of BNB MCP Server projects has been made public for users exploring the ecosystem.
As infrastructure matures, competitive advantages for developer-first companies will shift from API design toward: who can offer richer, more diverse, and composable toolsets.
In the future, every app could become an MCP client, and every API could serve as an MCP server.
This could give rise to new pricing mechanisms: Agents dynamically selecting tools based on execution speed, cost efficiency, relevance, etc., forming a more efficient Agent service economy powered by crypto and blockchain as the medium.
Of course, MCP itself does not directly serve end users—it is a foundational protocol layer. That means MCP’s true value and potential can only be realized when AI Agents integrate it and transform it into practical applications.
In the end, Agents are the carriers and amplifiers of MCP’s capabilities, while blockchain and cryptographic mechanisms build a trustworthy, efficient, and composable economic system for this intelligent network.
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