find_similar_regulations

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions the matching is 1:N, excludes base institution by default, and uses a similarity threshold. However, it omits details such as what happens when no similar regulations are found, potential rate limits, authentication requirements, or the exact nature of the return values. This is adequate but not comprehensive.

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 concise: two sentences that front-load the core action and use case. It is efficient and avoids fluff, but could be slightly more structured with bullet points or separated sections to improve scanability.

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?

The description lacks detail about the output format (e.g., does it return regulation IDs, titles, similarity scores?) and does not mention pagination, sorting, or error handling. Given the absence of an output schema and the tool's moderate complexity (6 parameters, 4 required), the description is insufficient for an agent to fully understand the tool's capabilities and expected results.

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?

The input schema has 100% description coverage, so the schema alone documents all parameters. The description provides a high-level framing (benchmarking, 1:N matching) but does not add significant detail beyond the schema's parameter descriptions. It repeats that 'threshold' is a similarity lower bound and 'excludeBase' excludes the same institution, which is already in the schema. Thus, baseline 3 is appropriate.

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 action: searching for N regulations from other institutions with high title similarity to a given base regulation, explicitly for benchmarking. It uses a specific verb ('검색') and resource ('유사도가 높은 다른 기관 규정'), and the 1:N matching concept distinguishes it from siblings like 'find_similar_precedents' or 'suggest_alio_benchmark'.

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 hints at the use case ('직접 벤치마킹용') but does not explicitly specify when to use this tool over alternatives like 'suggest_alio_benchmark' or 'chain_alio_benchmark'. It provides implied context but lacks clear guidance on when-not-to-use or explicit alternatives.

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