Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, and the description adds useful behavioral context: it is free, returns everything in one response ('한 응답에 포함합니다'), and enumerates the fields included. No contradiction with annotations exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the main purpose, but it repeats information in English and Korean ('List available text-generation LLM models' / '텍스트 생성 모델 카탈로그를 반환합니다') and duplicates the free indicator as '무료입니다' and '[무료]'. Useful details remain, but the repetition keeps it from being tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description compensates by listing the returned fields: pricing, family, size, multimodal flag, tags, use_cases, and max_context. It also states the intended relationship to llm_chat. Minor gaps like filter combination behavior and exact output shape are acceptable for a simple read-only catalog tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with each parameter already documented with its allowed values (e.g., tag as 'reasoning'|'coder'). The description does not add meaning beyond the schema; this matches the baseline of 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: 'List available text-generation LLM models with per-token pricing and max context.' The Korean portion adds concrete output contents, and the tool is clearly distinct from siblings like llm_chat, image_generate, and text_polish.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'llm_chat Tool의 model 입력값을 찾을 때 사용합니다' (use it when finding the model input for the llm_chat tool). It provides clear context but does not explicitly describe when not to use it or name alternative catalog tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.