Docs
  • Welcome
  • Background
    • What is MCP?
    • The Rise and Challenges of MCP
  • Introduction to DeMCP
    • What is DeMCP?
    • Core Capabilities and Advantages of DeMCP
  • DeMCP Development Roadmap
    • Q2 2025 Phase 1
    • Q3 2025 Phase 2
    • Q4 2025 Phase 3
    • Conclusion
    • Development Progress
  • DeMCP Detailed Technical Blueprint
    • DeMCP Detailed Technical Blueprint
    • Phase 1: SSE Proxy Layer and Centralized MCP Service Registry System
    • Phase 2: TEE Secure Environment and Blockchain Registry
    • Phase 3: Decentralized MCP Chain Ecosystem
  • Pricing
    • LLM Pricing
  • User Guide
    • Account Login
    • Service List
    • Purchase Credits
    • Create API KEY
    • Use LLM services or MCP services.
    • Billing
    • Usage Logs
Powered by GitBook
On this page
  • However, as its scale expands, MCP faces three core challenges in terms of infrastructure and ecosystem sustainability:
  • The Mission of DeMCP
  1. Background

The Rise and Challenges of MCP

PreviousWhat is MCP?NextWhat is DeMCP?

Last updated 29 days ago

• MCP (Model Context Protocol), since its official release by Anthropic on November 25, 2024, has been hailed as the “USB-C interface of AI,” providing a unified, standardized way for AI models to access external data sources and tools.

• By early 2025, the MCP ecosystem experienced explosive growth, with the number of MCPs quickly surpassing 1,000, making it a key enabler in the Agent industry and AI applications.

• MCP has now emerged as the widely recognized winner of the “Agent Open Standards War” from 2023 to 2025. At its current pace, the number of GitHub Stars for MCP is expected to surpass that of OpenAPI by July.

However, as its scale expands, MCP faces three core challenges in terms of infrastructure and ecosystem sustainability:

1.Limitations of Underlying Technical Models

• Most MCP services still rely on the Stdio communication model, which struggles to support distributed deployment and elastic scaling.

• The lack of SSE (Server-Sent Events) mode support hinders remote service integration and the “modular building block” approach, limiting further evolution in AI agents.

2.User Trust and Security Concerns

• When using remote MCP services, users are increasingly worried about data security, privacy protection, and service transparency.

• The ecosystem lacks robust mechanisms for service discovery, auditing, and oversight, making it difficult to provide users with sufficient trust assurances.

3.Unclear Incentive Mechanisms and Revenue Distribution

• MCP creators struggle to receive fair compensation based on service usage due to the absence of sustainable revenue-sharing models, causing many potential developers to adopt a wait-and-see attitude toward long-term investment.

• For Agent platforms, the insufficient supply of high-quality MCP services stifles ecosystem vitality and the full unleashing of innovation potential.

The Mission of DeMCP

To address the aforementioned challenges, DeMCP was born. Leveraging blockchain and TEE (Trusted Execution Environment) technologies, it provides the MCP ecosystem with a secure, scalable infrastructure equipped with robust incentive mechanisms. DeMCP aims to propel the MCP ecosystem beyond a mere tool interface, transforming it into a truly decentralized, sustainable, and thriving network.