In the previous article, we discussed how DePIN+AI sparked widespread community discussions. Today, let's continue the Crypto+AI conversation. Another major hot topic currently in the community is the MCP Protocol. Thanks to its breakthroughs in cross-chain communication and distributed computing, the topic of “AI Agents” has been thrust back into the spotlight. Let’s dive into what exactly MCP is, why it could ignite a new AI Agent boom, and how the community is currently discussing it.
What Is the MCP Protocol?
The Model Context Protocol (MCP) was launched by Anthropic in November 2024 as an open-source standard designed to establish a unified, two-way context exchange interface between large language models (LLMs) and external tools or data sources.
MCP acts like the “USB-C interface” for AI applications, enabling any AI model following the protocol to easily access various data sources, function plugins, and services.
With major players like OpenAI and Google DeepMind successively adopting MCP, the community and developer ecosystems have rapidly expanded, sparking a new wave of innovation and discussion around AI Agents.
In essence, MCP is a blockchain-based framework for cross-chain communication and multiparty computation. Its two core ideas are:
Free-flowing on-chain messages: Whether on Ethereum, BSC, or Solana, MCP allows decentralized interoperability of on-chain data and instructions.
Secure multiparty computation: Sensitive data is shard-stored and computed across different nodes, ensuring both privacy and enhanced computational efficiency.
Put simply, MCP is a protocol that allows different AI models to "talk" and borrow each other’s context. Imagine running a text summarization AI, feeding the summary into a sentiment analysis AI, and then passing the analysis to another AI for action recommendations—MCP is the "highway" connecting them. It standardizes the input-output of various models, allowing them to combine like Lego bricks.
Core Components
- MCP Server
The MCP server declares available tools (such as databases, file systems, computation services) and their interface specifications for AI Agents to retrieve and call. It exposes the tool registry, API call methods, and security authentication mechanisms via open APIs (typically based on HTTPS), returning results in JSON format.
- MCP Client
The MCP client is embedded within AI Agents or applications. It is responsible for discovering servers, parsing tool descriptions, and invoking corresponding interfaces during conversations or task executions. Clients are equipped with heartbeat detection and retry strategies to ensure seamless failover if tools become unavailable or response timeouts occur.
- Authentication and Authorization
MCP defines authentication flows based on OAuth 2.0 or API Keys, ensuring only authorized Agents can access sensitive data sources. For high-security scenarios, it supports zero-trust architecture and Role-Based Access Control (RBAC) to further refine permissions across tools and datasets.
Why MCP Rapidly Ignited the Second AI Agent Boom
- Seamless Context Flow
Traditional AI Agents often rely on manually stitched APIs and custom middleware, leading to “context loss” and “state isolation” issues.
MCP solves this by unifying context description formats, allowing models to transition between multi-turn dialogues or multi-task scenarios without repeated loading or manual context translation—greatly enhancing coherence.
This is crucial for complex Agents: from text summarization to sentiment analysis to action recommendations, each step can seamlessly hand over to different models and tools without information fragmentation.
- Multimodal and Multiscenario Access
MCP supports not only text models but also integrates with multimodal applications like image, voice, and sensor data. For example, through IoT servers or hardware interfaces, robotic nodes, edge cameras, and sensor data can all be registered as MCP tools, allowing Agents to directly call visual/audio recognition services.This enables AI Agents to mix multiple capabilities within a single session: OCR text extraction, scene visual understanding, audio transcription, and command execution—all in one flow.
- Ecosystem-Driven Operations and Low-Barrier Development
Since MCP defines a unified "tool discovery" and "function invocation" interface, developers only need to write protocol-compliant adapters to connect their services into the ecosystem.Open-source projects like PydanticAI and Cursor have already released MCP plugins, allowing community contributors to deploy new tool services as easily as using Docker images.At the same time, incentive mechanisms like token rewards or contributor rankings are being explored, promising ongoing revenue streams for node operators and service providers—further invigorating the ecosystem.
Key Features and Advantages of MCP
- Standardized Interfaces and Compatibility
MCP adopts open standards and provides multi-language SDKs (Python, TypeScript, C#, etc.), ensuring developers can integrate it across different tech stacks.Whether using Anthropic Claude, OpenAI’s GPT series, Google Gemini, or custom-built models, as long as MCP compliance is ensured, they can interoperate within the same tool ecosystem.
- Security and Controllability
According to ArXiv research, strict auditing during MCP’s lifecycle management (creation, operation, and update stages) is necessary to prevent injection attacks and permission sprawl.The protocol recommends deploying behavior monitoring and log auditing at critical points, and enforcing multi-factor authentication or multisignature authorization for sensitive tool calls—ensuring data security from the ground up.
- Scalable Tool and Service Ecosystem
Google Cloud’s ADK now supports MCP, enabling databases, BI tools, and enterprise applications to be called by Agents as standard tools.
The community-maintained “awesome-mcp-servers” repository showcases implementations for IoT devices, 3D printers, smart home systems, and more.
Outlook: Prospects for the Second AI Agent Boom
MCP provides "universal access" and "multiscenario chaining" infrastructure for AI Agents, inevitably spawning a wealth of innovative applications—including serverless cloud robots, autonomous inspections, intelligent customer service, and industry-specific assistants.
In the next phase, Agents will not only collaborate across applications but may also discover and team up within distributed networks, forming task alliances to jointly complete complex projects.
In AI security research, we will likely see specialized MCP auditing tools and sandbox environments emerge, ensuring that Agent execution paths are traceable, reversible, and accountable.
If the ecosystem can successfully address security, compatibility, and commercialization challenges, MCP is poised to become the new "foundational protocol" for AI Agents, much like TCP/IP for the internet—laying the groundwork for intelligent interconnected systems.
Conclusion
The Model Context Protocol standardizes interactions between AI and the external world, liberating Agents from isolated silos and equipping them with universal interfaces, expandable ecosystems, and strong security guarantees.
With increasing support from mainstream companies and community projects, MCP will push AI Agents beyond proof-of-concept toward large-scale deployments, driving a new efficiency revolution in smart offices, smart cities, industrial automation, and beyond.
In the future, Agents built on MCP will no longer just be "talking robots"—they will become digital actors capable of autonomous sensing, decision-making, and collaboration, fully realizing the catalytic synergy between humans and machines.