aiRltLs_search

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

The annotations indicate read-only, idempotent, and non-destructive behavior, which the description doesn't contradict. The description adds valuable context beyond annotations: it specifies that the tool is 'AI-powered,' returns a 'list of semantically related law articles,' and notes that JSON is not supported (only XML). This provides practical usage details that annotations alone don't cover, though it could mention rate limits or auth needs.

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 well-structured with clear sections (purpose, usage, args, returns, example) and uses bullet points for readability. It's appropriately sized, with every sentence adding value, such as the example that illustrates input and output. Minor improvements could include streamlining the bilingual text, but overall it's efficient and front-loaded with key information.

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 complexity (4 parameters, 0% schema coverage, no output schema) and rich annotations, the description is largely complete. It covers purpose, usage, parameters, and output format, though it lacks details on error handling or pagination. The example helps clarify usage, but without an output schema, more on return structure could be beneficial. Still, it provides sufficient context for effective use.

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?

With 0% schema description coverage, the description fully compensates by explaining all parameters in detail. It defines 'query' as a 'law name or keyword,' 'search' with scope options (0 for law articles, 1 for administrative rules), 'oc' as an optional override, and 'type' as response format (XML only). This adds essential meaning beyond the bare schema, ensuring parameters are well-understood.

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 explicitly states the tool's purpose as 'discovering related laws from vague topics' and 'finds laws semantically related to a given law name or keyword,' using specific verbs ('discovering,' 'finds') and resources ('laws,' 'law articles'). It clearly distinguishes this from sibling tools by labeling it as the '⭐ PREFERRED TOOL' for this specific use case, differentiating it from other search tools in the list.

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 provides explicit guidance on when to use this tool ('when user wants to explore laws around a general subject') and includes a 'Best for' section with examples (e.g., '민법' → 상법, 의료법, 소송촉진법). It also implicitly suggests alternatives by noting it's the 'PREFERRED TOOL' for this task, implying other tools might exist for different scenarios, though it doesn't name specific alternatives.

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