Fearful Moments: Analyzing the Seven Structural Contradictions of the MCP Protocol in AI Collaboration

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 extend this further:

1. The tool explosion problem is real: The MCP protocol standard has led to an overwhelming abundance of linkable tools. LLMs find it difficult to effectively select and use so many tools, and no AI can master all professional fields simultaneously; this is not a problem that can be solved by parameter quantity.

2. The gap described in the document: 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 Achilles' heel of the dual-interface architecture: MCP, as middleware between LLM and data sources, has to handle upstream requests while converting downstream data, which presents inherent deficiencies in this architectural design. When data sources explode, unified processing logic becomes nearly impossible.

4. The return structures vary greatly: the lack of standardization leads to chaotic data formats. This is not a simple engineering issue, but rather a result of the overall absence of industry collaboration, which requires time.

5. Context window limitation: No matter how fast the token limit grows, the information overload problem always exists. MCP outputting a bunch of JSON data takes up a lot of context space, squeezing inference capability.

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

7. The difficulty of connecting multiple MCP servers: "The biggest challenge is that it is complex to chain MCPs together." This difficulty is not groundless. Although MCP itself is unified as a standard protocol, the specific implementation of each server in reality is different, one processes files, one connects to APIs, one operates databases... When AI needs to collaborate across servers to accomplish complex tasks, it's as difficult as trying to force Lego, bricks, and magnets together.

8. The emergence of A2A is just the beginning: MCP is only 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.

The above.

These issues actually reflect the growing pains during the transition of AI from a "tool library" to an "AI ecosystem." The industry is still at the initial stage of just throwing tools at AI, rather than building a true AI collaborative infrastructure.

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

Just welcome to the new world.