Знайти FAQ за запитом — hotline.finance

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

With no annotations, the description carries the full burden. It discloses a key behavioral trait: reliance on LLM sampling and a fallback response when sampling is not supported. However, it omits details such as whether the operation is read-only, the shape of returned questions, or behavior when no category matches, leaving gaps in transparency.

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 two sentences, front-loaded with the primary action and includes a concise fallback clause. Every sentence contributes value, with no redundancy or filler, making it concise and well-structured.

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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description provides a reasonable overview: input, classification mechanism, fallback behavior, and output type (relevant questions or category hint). It lacks some return value detail, but for a straightforward lookup/classifier it is sufficiently complete.

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%, so the baseline is 3. The description's mention of 'arbitrary text query in Ukrainian' adds no new meaning beyond what the schema already provides with its examples. Thus, the parameter semantics are adequately covered by the schema without extra contribution from the description.

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 clearly states the tool accepts a free-text Ukrainian query, classifies it via LLM into an FAQ category, and returns relevant questions from hotline.finance. This specific verb+resource+behavior distinguishes it from siblings like list_faq_categories and get_faq_questions, which are more direct browsing tools.

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 implicitly indicates this tool is for natural-language queries, contrasting with list_faq_categories or get_faq_questions that likely require structured category selection. It also mentions a fallback when sampling is unsupported, giving contextual guidance, but it does not explicitly exclude alternatives or name them as usage options.

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