I have learned that the analysis of the dilemmas surrounding MCP is quite on point, hitting the pain points directly, revealing that the implementation of MCP is a long and arduous journey, and it is not that easy. I would like to expand on this:

1) The tool explosion problem is real: The MCP protocol standard has led to an overwhelming proliferation of tools that can be linked. LLMs find it difficult to effectively choose and use so many tools, and no AI can be proficient in all professional fields at the same time; this is not a problem that can be solved by simply increasing parameter counts.

2) The documentation description gap: There is still a significant disconnect between technical documentation and AI understanding. Most API documentation is written for humans, not for AI, lacking semantic descriptions.

3) The weakness of the dual-interface architecture: As middleware between LLMs and data sources, MCP must handle upstream requests and transform downstream data, which presents inherent design flaws. When data sources explode, unified processing logic becomes nearly impossible.

4) Diverse return structures: The lack of standardization leads to chaotic data formats. This is not a simple engineering problem but a result of the overall lack of industry collaboration, which requires time to address.

5) Limited context window: Regardless of how quickly the token limit increases, the issue of information overload always exists. When MCP outputs a large amount of JSON data, it occupies a significant amount of context space, squeezing reasoning capabilities.

6) Flattening of nested structures: Complex object structures lose their hierarchical relationships in text descriptions, making it difficult for AI to reconstruct the interrelations of the data.

7) The difficulty of linking multiple MCP servers: "The biggest challenge is that it is complex to chain MCPs together." This difficulty is not unfounded. Although MCP is unified as a standard protocol, the specific implementations of various servers differ in reality—one processes files, another connects APIs, and another operates databases… When AI needs to collaborate across servers to complete complex tasks, it is as challenging as trying to forcefully fit Lego, building blocks, and magnetic tiles together.

8) The emergence of A2A is just the beginning: MCP is merely the initial stage of AI-to-AI communication. A true AI Agent network requires higher-level collaboration protocols and consensus mechanisms; A2A may just be an excellent iteration.

In summary, these issues reflect the growing pains of AI transitioning from a "tool library" to an "AI ecosystem." The industry is still at the initial stage of throwing tools at AI rather than building a true AI collaborative infrastructure.

Therefore, it is necessary to demystify MCP, but we should not overlook its value as a transitional technology.

Just welcome to the new world.

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