Original author: Haotian (X: @tme l0 211 )
These analyses of the dilemma of MCP are quite accurate and directly hit the pain points, revealing that the implementation of MCP is a long and difficult process. I will also expand on this:
1) The tool explosion problem is real: MCP protocol standards and linked tools are flooding the market. It is difficult for LLM to effectively select and use so many tools. No AI can be proficient in all professional fields at the same time. This is not a problem that can be solved by parameter quantity.
2) Documentation gap: There is still a huge gap between technical documentation and AI understanding. Most API documents are written for humans, not for AI, and lack semantic descriptions.
3) The weakness of the dual-interface architecture: MCP, as the middleware between LLM and the data source, must process upstream requests and transform downstream data. This architectural design is inherently insufficient. When the data source explodes, it is almost impossible to unify the processing logic.
4) The returned structures vary widely: Inconsistent standards lead to confusing data formats. This is not a simple engineering problem, but the result of an overall lack of industry collaboration, which takes time.
5) Limited context window: No matter how fast the token limit grows, the information overload problem always exists. MCP spits out a bunch of JSON data, which takes up a lot of context space and squeezes 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 correlation between data.
7) Difficulty in linking multiple MCP servers: The biggest challenge is that it is complex to chain MCPs together. This difficulty is not groundless. Although MCP is a unified standard protocol, the specific implementations of each server are different in reality. One handles files, one connects to APIs, and one operates databases... When AI needs to collaborate across servers to complete complex tasks, it is as difficult as trying to force Lego, building blocks, and magnetic pieces 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 a higher level of collaboration protocols and consensus mechanisms, and A2A may just be an excellent iteration.
above.
These problems actually reflect the pain of AIs transition from tool library to AI ecosystem. The industry is still in the early stages of throwing tools to AI, rather than building a true AI collaborative infrastructure.
Therefore, it is necessary to demystify MCP, but its value as a transitional technology should also be ignored.
Just welcome to the new world.
