Original Author: Haotian (X: @tme l0 211 ）
These analyses of MCP's dilemmas are quite on point, hitting the pain points and revealing that MCP's implementation path is long and not easy. Let me expand on this:
1) Tool explosion is real: The MCP protocol standard has an overwhelming number of connectable tools. LLMs find it difficult to effectively select and use so many tools, and no AI can be proficient in all professional domains, which is not a problem that can be solved by parameter count.
2) Documentation gap: There is a huge disconnect between technical documentation and AI understanding. Most API documents are written for humans, not for AI, lacking semantic descriptions.
3) Weakness of dual-interface architecture: As middleware between LLM and data sources, MCP must handle upstream requests and transform downstream data. This architectural design is inherently insufficient. When data sources explode, unified processing logic is almost impossible.
4) Diverse return structures: Lack of standards leads to chaotic data formats, which is not a simple engineering issue but the result of overall industry collaboration absence, requiring time.
5) Context window limitations: Regardless of how quickly Token limits grow, information overload always exists. MCP spewing out a bunch of JSON data will occupy a large context space, squeezing inference capabilities.
6) Nested structure flattening: Complex object structures lose hierarchical relationships in text descriptions, making it difficult for AI to reconstruct data correlations.
7) Difficulty of multi-MCP server connections: "The biggest challenge is that it is complex to chain MCPs together." This difficulty is not groundless. Although MCP as a standard protocol is unified, the specific implementations of servers in reality are different. One handles files, another connects to APIs, another operates databases... When AI needs to collaborate across servers to complete complex tasks, it's as difficult as trying to forcibly connect Lego, building blocks, and magnetic pieces.
8) A2A's emergence is just the beginning: MCP is only the primary stage of AI-to-AI communication. A true AI Agent network requires higher-level collaborative protocols and consensus mechanisms. A2A might just be an excellent iteration.
These issues actually centrally reflect the growing pains of AI's transition from a "tool library" to an "AI ecosystem". The industry is still in the primitive stage of throwing tools at AI, rather than building genuine AI collaboration infrastructure.
So, demystifying MCP is necessary, but don't overlook its value as a transitional technology.
Just welcome to the new world.
