What difficulties does MCP face on its long road to implementation?

Question

> It is necessary to demystify MCP, but do not overlook its value as a transitional technology.  **Written by: Haotian**  I 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, which is not that easy. I will extend on this:  1) The issue of tool explosion is real: the MCP protocol standard has led to an overwhelming proliferation of linkable tools, making it difficult for LLMs to effectively select and use so many tools. Moreover, no AI can be proficient in all specialized fields at the same time; this is not a problem that can be solved by simply increasing parameter count.  2) Document Describing the Gap: There is still a significant gap 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 and transform downstream data, leading to inherent deficiencies in this architectural design. When data sources explode, unified processing logic becomes nearly impossible.  4) The return structures are vastly different: The lack of standardization has led to a chaotic data format, which is not just a simple engineering problem, but rather a result of the overall absence of industry collaboration, and it requires time.  5) Context window limitations: Regardless of how quickly the token limit increases, the information overload problem always exists. MCP outputting a bunch of JSON data can take up a lot of context space, squeezing inference capability.  6) Flattening nested structures: Complex object structures may lose their hierarchical relationships in text descriptions, making it difficult for AI to reconstruct the associations between 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 as a standard protocol is unified, the specific implementations of each server in reality vary widely; one processes files, another connects APIs, and another operates databases... When AI needs to collaborate across servers to complete complex tasks, it is as difficult as trying to forcibly combine Lego, building blocks, and magnetic tiles.  8) The emergence of A2A is just the beginning: MCP is merely the primary 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.  Above.  These issues actually reflect the growing pains of AI transitioning from a "toolbox" to an "AI ecosystem." The industry is still at the primitive stage of just handing tools to 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.