The road to MCP implementation is long. What difficulties does it face?

1) The tool explosion problem 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 description 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 the dual-interface architecture: As a middleware between LLM and data sources, MCP must handle upstream requests and transform downstream data. This architectural design is inherently flawed. When data sources explode, unified processing logic is almost impossible. 4) Vastly different return structures: Lack of standards leads to data format chaos, 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 unfounded. Although MCP as a standard protocol is unified, the specific implementations of servers in reality are different. One handles files, one connects to APIs, one 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.