Every LLM framework describes tools in its own way

In modern application development using Large Language Models (LLMs), a problem of tool fragmentation has emerged. Developers are forced to rewrite the same functions to meet the requirements of different frameworks, such as AI SDK, MCP servers, or Genkit. While the logic of the tools themselves remains unchanged, each framework requires unique wrappers, argument schemas, and registration methods. This leads to code duplication and complicates project maintenance when switching or adding new orchestration tools. The author raises the question of the need for standardizing tool definitions for LLMs to avoid redundant work and simplify function integration across various ecosystems. The current situation forces teams to spend resources on code adaptation rather than functional development, highlighting the need for a unified approach to defining tools in the world of AI development.