aiSearch

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, indicating a safe, repeatable read operation. The description adds valuable context beyond annotations: it specifies that it 'Returns FULL ARTICLE TEXT (조문내용)' and notes 'Response format - XML only (JSON not supported),' which are important behavioral traits not covered by annotations.

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 and front-loaded with key information: it starts with usage priority, states the purpose, details parameters with examples, and ends with a return value summary and example. Every sentence adds value, with no redundant or wasted content.

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 (6 parameters, 0% schema coverage, no output schema), the description is highly complete. It covers purpose, usage guidelines, parameter semantics, return values ('Returns FULL ARTICLE TEXT'), and format constraints ('XML only'). The example further clarifies usage. With annotations providing safety context, this description leaves minimal gaps.

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 0%, so the description carries the full burden of parameter documentation. It provides clear semantics for all 6 parameters: query is described as 'Search query (natural language supported),' search has enumerated scope options with defaults, display as 'Results per page,' page as 'Page number,' oc as 'Optional OC override,' and type as 'Response format - XML only.' This compensates well for the lack of schema descriptions.

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's purpose: '지능형 법령검색 시스템 검색 API (AI-powered semantic law search)' and 'Uses intelligent/semantic search to find relevant law articles.' It distinguishes itself from siblings by explicitly mentioning 'more comprehensive than eflaw_search' and being the 'PREFERRED TOOL for vague or natural language queries.'

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 usage guidelines: 'Use this FIRST when user's intent is unclear or conversational' and 'Best for: Natural language queries like "뺑소니 처벌", "음주운전 벌금".' It also distinguishes from alternatives by noting it's 'more comprehensive than eflaw_search,' helping the agent choose between sibling tools.

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