The Rise of MCP: How DeMCP Is Powering the Next AI-Agent Revolution

MCP, or the Model Context Protocol, is rapidly emerging as a focal point in the AI field, gaining significant attention and momentum. Recently, Sam Altman, CEO of OpenAI, announced on X that OpenAI will fully support MCP, having integrated it into the Agents SDK, with plans to soon extend it to the ChatGPT — a powerful endorsement that underscores MCP’s growing prominence. Currently, numerous leading companies in the AI industry, including Microsoft, Cursor and Apollo have joined the ranks of supporters, demonstrating MCP’s widespread acceptance and strong potential as a new standard for AI tool integration. This surge of enthusiasm signals that MCP is poised to become a foundational protocol for building more intelligent, context-aware AI systems.

MCP is developed by Anthropic in November 2024, designed to revolutionize how AI models interact with external data sources and tools. Often likened to a "USB-C for AI," MCP provides a universal, standardized interface that enables seamless, secure, and real-time communication between AI applications and diverse systems—such as Google Drive, Slack, GitHub, or custom databases—eliminating the need for fragmented, custom integrations. MCP allows AI agents to dynamically access context, retrieve data, and perform actions, enhancing their functionality and adaptability across various domains. For example, with Blender, integrating the MCP protocol allows users to generate complex 3D models simply by entering text in an LLM interface.

(https://x.com/minchoi/status/1900379164454101154)

The MCP infrastructure remains underdeveloped, facing key challenges. Most MCPs still rely on local stdio mode, limiting remote access and scalability, which hinders modular integration. Security and privacy concerns persist due to the lack of supervision and safeguards, posing risks from version changes or malicious server behavior. Additionally, unclear revenue-sharing and incentives make sustainable development difficult, leading to a shortage of high-quality MCP services. DeMCP addresses these issues by leveraging blockchain and TEE technology to provide a secure, scalable, and incentivized MCP infrastructure.

DeMCP is the first decentralized MCP ecosystem. It provides a standardized MCP protocol and seamless integration with major LLMs, empowering MCP developers to easily build, deploy, and commercialize MCPs—similar to an App Store for MCP services.For Agent developers, DeMCP offers a wide range of secure, self-developed, and third-party remote MCPs, along with 10+ LLM APIs, enabling cost-effective, one-stop intelligent agent creation.By leveraging Trusted Execution Environment (TEE) technology, DeMCP ensures the secure deployment of MCP servers. Users can access MCP services remotely without local installation, reducing compatibility issues and technical barriers while enabling dynamic scalability. TEE provides hardware-level isolation, preventing unauthorized access or tampering of MCP services. With blockchain-based registries enhancing transparency and immutability, DeMCP fundamentally redefines the security and reliability of MCP at the infrastructure level.

In addressing the economic and incentive challenges of the MCP ecosystem, DeMCP introduces a revenue-sharing model based on usage and tool quality, ensuring sustainable rewards for high-quality MCP service developers. Additionally, token incentives and an ecosystem fund attract more developers, fostering MCP innovation and diverse real-world applications. To further support global adoption, DeMCP offers an LLM subsidy program, allowing developers worldwide to access top AI models at a lower cost. All MCP and LLM services on the platform are paid for using crypto, reducing entry barriers for developers across the globe.

DeMCP has already integrated 50+ Web3 MCP services and 10+ LLM APIs. MCP services include on-chain data queries for BSC, Base, and Solana, token trading via Uniswap and Jupiter, as well as social media management for X and Telegram. LLMs include GPT-4o, GPTo1, Gemini-1.5, and Claude 3.7 Sonnet, among other top-tier models. With access to these diverse MCP and LLM services, Agent developers can efficiently and cost-effectively build and combine capabilities tailored to specific use cases.

MCP is emerging as one of the most significant trends in AI, attracting a growing number of developers and AI companies. Unlike traditional Web2, Web3 has a fundamentally different architecture and user experience, demanding a specialized on-chain MCP solution. DeMCP aims to become the MCP hub for Web3, enabling Agent developers to seamlessly and flexibly assemble various Web3 Agents with zero barriers. This will unlock tremendous ecosystem value and commercial potential for the Web3 space.