According to PANews, the MCP protocol faces several challenges as it attempts to integrate into AI ecosystems. The protocol, designed to link various tools, struggles with an overwhelming number of available options, making it difficult for large language models (LLMs) to effectively choose and utilize them. No AI can master all professional fields, and this issue cannot be resolved by increasing parameter counts.
A significant gap exists between technical documentation and AI comprehension, as most API documents are written for human understanding and lack semantic descriptions. The dual-interface architecture of MCP, which acts as middleware between LLMs and data sources, is inherently flawed. It must handle upstream requests and transform downstream data, a task that becomes nearly impossible when data sources proliferate.
The lack of standardization leads to inconsistent data formats, a problem stemming from the absence of industry-wide collaboration. This issue requires time to resolve. Despite increases in token limits, information overload remains a persistent problem, as MCP outputs large amounts of JSON data that consume significant context space, limiting inference capabilities.
Complex object structures lose their hierarchical relationships in text descriptions, making it difficult for AI to reconstruct data associations. The challenge of linking multiple MCP servers is significant, as each server may handle different tasks, such as file processing, API connections, or database operations. When AI needs to collaborate across servers, it is akin to forcing disparate building blocks to fit together.
The emergence of AI-to-AI (A2A) communication marks only the beginning of a more advanced AI agent network, which will require higher-level collaboration protocols and consensus mechanisms. MCP represents an initial stage in this evolution.
These challenges highlight the growing pains of transitioning from an AI 'tool library' to a fully integrated AI ecosystem. The industry remains in an early phase of providing tools to AI rather than building a true AI collaboration infrastructure. While it is important to demystify MCP, its value as a transitional technology should not be overlooked.