What difficulties does MCP face on its long road to implementation?
It is necessary to demystify MCP, but do not overlook its value as a transitional technology.Author: Haotian
I have learned that the analysis of the dilemmas surrounding MCP is quite on point, hitting the pain points and revealing that the implementation of MCP is a long and difficult road. I would like to expand on this:
1) The tool explosion problem is real: The MCP protocol standard has led to an overwhelming number 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 specialized fields at the same time; this is not a problem that can be solved by parameter size.
2) The documentation description gap: There is still a huge disconnect between technical documentation and AI understanding. Most API documentation is written for humans, not for AI, lacking semantic descriptions.
3) The soft spot of the dual-interface architecture: As middleware between LLMs and data sources, MCP has to handle upstream requests and transform downstream data, which makes this architectural design inherently flawed. When data sources explode, unified processing logic becomes nearly impossible.
4) Varied 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.
5) Limited context window: No matter how fast the token limit grows, the issue of information overload always exists. MCP outputting a bunch of JSON data will consume a large amount of context space, squeezing reasoning capabilities.
6) Flattening nested structures: Complex object structures lose their hierarchical relationships in text descriptions, making it difficult for AI to reconstruct the relationships between 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 handles 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 piece together Lego, building blocks, and magnetic tiles.
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.
That's all.
These issues reflect the growing pains of AI transitioning from a "tool library" to an "AI ecosystem." The industry is still at the primary stage of throwing tools at AI, rather than building a true AI collaboration infrastructure.
Therefore, it is necessary to demystify MCP, but do not overlook its value as a transitional technology.
Just welcome to the new world.
