get_intelligent_related_laws

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

With no annotations, the description carries full burden. It transparently explains AI-based semantic matching, absence of direct text matching, and output is list of article titles. Behavior is well-disclosed, though read-only nature could be more explicit.

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?

Description is three sentences: core purpose, sibling distinction, and follow-up action. Every sentence adds value, no redundancy or fluff. Ideal length for quick agent comprehension.

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?

No output schema, but description states return is list of article titles. Parameters are fully documented. Follow-up action provided. Missing details like pagination (though display parameter suggests limit), but overall sufficient for a simple recommendation 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% (all 3 parameters described). The description repeats schema info without adding significant new meaning beyond mentioning broader matching for query. Baseline 3 is appropriate as description doesn't substantially extend schema understanding.

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?

Description clearly states the tool returns AI-recommended related laws (조문 list) based on keyword semantic similarity. It distinguishes itself from sibling intelligent_law_search by noting broader matching, making the purpose specific 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?

Description contrasts this tool with intelligent_law_search (broader matching) and advises follow-up with get_law_text(lawId) for full text. While it hints at when to use, it lacks explicit when-not-to-use instructions, resulting in good but not perfect guidance.

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