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

The readOnlyHint annotation already signals a safe read operation, and the description adds meaningful behavioral detail: it lists exactly what the response contains (1M-token input/output prices in points, family, size, multimodal flag, tags, use_cases, max_context), states that everything is returned in one response, and notes that the tool is free. This goes well beyond the annotation without contradicting it.

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 reasonably structured but contains redundancy: the English opening is restated in Korean, and the free-of-charge point appears twice ('무료입니다' and '[무료]'). The key detail about llm_chat is placed late, though the response-content list is front-loaded. It is compact but not maximally efficient.

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 must carry the return-value explanation, and it does: it enumerates the exact fields returned, states the pricing basis (per 1M tokens, in points), says results come in one response, and explains the tool's relationship to llm_chat. Together with the fully documented input schema, an agent has enough to select and invoke this tool correctly.

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%, and each of the four optional parameters already has its own clear description and allowed values. The tool description does not add parameter-level meaning beyond that, so the baseline score of 3 is appropriate: the schema does the heavy lifting.

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.' It also distinguishes itself from the sibling llm_chat by explicitly stating it is used to find the model input value for llm_chat. This makes the tool's role as a catalog clear and unambiguous.

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 gives an explicit usage condition: 'llm_chat Tool의 model 입력값을 찾을 때 사용합니다' ('Use when finding the model input value for the llm_chat Tool'). It names the relevant sibling tool directly, and since no other sibling provides model catalog data, this acts as complete routing guidance.

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