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

No annotations are provided, so the description must carry the transparency burden. It discloses the read-only nature (조회) and the full-text scope, but omits details about output structure, error behavior, or permissions.

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 a single, concise sentence that front-loads the action and then provides usage context, with no filler.

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?

For a simple fetch-by-ID tool, the description adequately covers the main usage and return scope. However, the absence of an output schema and details about failure cases leaves minor gaps in completeness.

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?

Input schema already fully documents both parameters (id and university) with descriptions. The description merely references the schema's ID source, adding no new semantic value beyond the schema.

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 retrieves the full text of a specific regulation ('특정 규정의 전문을 조회합니다'), distinguishing it from search/list tools and the article-level sibling by focusing on full-text retrieval.

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?

It provides explicit prerequisite guidance: use the ID obtained from search_regulations or list_regulations. This implies when to use the tool (after a search/list) but does not explicitly mention when not to use it or alternatives for article-level retrieval.

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

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

With no annotations, the description carries the transparency burden. It indicates a read-only lookup (조회) and scoping to a single article, but it does not disclose return format, error behavior, or access requirements.

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 a single concise sentence that states purpose and inputs, with no redundancy. Every word earns its place.

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?

For a simple retrieval tool with good schema but no output schema, the description is adequate but not comprehensive; it omits the return value and any caveats, which would be helpful for an agent.

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 coverage is 100%, so the schema already documents all parameters. The description adds minimal extra meaning by associating 'regulation id' and 'article number' to the id/article parameters, but it does not mention the university parameter.

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 that this tool retrieves only a specific article of a regulation, distinguishing it from siblings like get_regulation and search_regulations. The verb '조회' (retrieve) and resource 'regulation article' make the purpose 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?

It instructs the user to enter the regulation id and article number, providing clear context for when to use this tool. However, it does not explicitly mention alternatives or exclusions, such as using get_regulation for the full regulation.

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

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

No annotations are provided, so the description carries the full burden. It discloses the conditional behavior (list of regulations vs. list of universities) but does not mention return format, pagination, or other potential side effects. As a simple read-only list tool, this is acceptable but not exceptionally transparent.

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 short sentences with no extraneous information. It front-loads the purpose and states the key conditional behavior efficiently. Every sentence earns its place.

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 low complexity and full parameter descriptions in the schema, the description is generally complete for a list tool. However, no output schema exists and the description doesn't mention return fields, but for a list of regulations this is likely acceptable. It also doesn't explain relation to sibling tools, but that is not critical here.

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% for both parameters, and the main description repeats the behavior of omitting 'university' that is already in the schema parameter description. No additional parameter semantics are added beyond what the schema provides, so the baseline of 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 tool returns a list of university regulations, with a specific scope: if 'university' is omitted, it shows supported universities. This is a specific verb+resource+scope. However, it does not explicitly differentiate itself from sibling tools like search_regulations, so it falls short of a 5.

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 implied usage context by explaining the behavior when 'university' is omitted, but it does not explicitly state when to use this tool versus alternatives such as search_regulations or get_regulation. No exclusions or alternative tool references are given, so there is no clear guidance.

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

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

With no annotations, the description carries full responsibility for behavioral disclosure. It states that search is limited to regulation names and returns a list, adding useful context. However, it does not clarify matching behavior (partial/exact, case sensitivity), pagination, or handling of no results, leaving some gaps.

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 consists of two concise sentences, front-loading the action and scope. Every word earns its place with no redundancy.

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 tool is simple with two fully described parameters, but lacks an output schema and annotations. The description explains the core search behavior and return type (list of regulations), but omits details about the structure of the returned list and edge cases, such as no matches. It is adequate for a basic tool but not fully 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?

The schema already provides descriptions for both parameters with 100% coverage, so the description does not need to add parameter details. It adds no extra meaning beyond the schema, which is the baseline for high coverage.

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 it searches university regulations by keyword, specifically looking in regulation names, and returns a list of matching regulations. This differentiates it from sibling tools that list all regulations or fetch specific ones.

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 clearly implies use when the user needs to find regulations by keyword, and the scope ('regulations name') is clear. However, it does not explicitly contrast with sibling tools or mention when not to use it, so it lacks explicit exclusions or alternatives.

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