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

The description reveals that it returns 'candidate' rules (implying possible multiple matches) and always notifies that revision date and effective date may differ. It also mentions the change summary behavior when compare_with_current is true, going beyond the basic read-only and idempotent 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 concise, front-loaded with the main purpose, followed by use cases and a note. It does not contain redundant information and is well-structured for quick understanding.

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 presence of an output schema (not shown here) and the thorough parameter explanations, the description is complete for an agent to use the tool. It covers all necessary aspects: what it does, when to use it, key caveats, and the primary alternative.

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?

All six parameters are described with meaningful context beyond the schema: date format, the relationship between law_id and rule_keyword, specific article examples, max_changes default and limit, and the boolean compare_with_current. The description also explains that date can be used without knowing HISTORY_ID, adding semantic clarity.

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 the tool returns candidate rules revised before a base date, along with the text at that time and a summary of changes. It also explicitly distinguishes from lookup_dongguk_rule for latest rules, making its purpose distinct among siblings.

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 specific use cases (retroactive work, audit responses, checking past payment standards) and states when not to use it (for latest rules, use lookup_dongguk_rule). However, it does not mention other alternatives like compare_rule_versions or list_rule_history, but the primary scenarios are well covered.

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?

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering the main behavioral characteristics. The description adds a small detail about comparing by article units but does not mention side effects, permissions, or other behavioral aspects beyond what annotations already provide.

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 extremely concise—two short sentences—with the primary action stated first and the key optional behavior immediately following. No unnecessary words or redundant explanations.

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 is adequate for a read-only comparison tool, especially since an output schema exists (though not shown) to define the return structure. It lacks only a brief note about the nature of the comparison result (e.g., 'returns a list of changed articles'), but this is not a critical gap given the scope.

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% with all six parameters individually described. The tool description adds a minor clarification about 'to_history_id' omission, but otherwise relies on the schema. Given complete schema coverage, 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 a specific verb ('compare'), resource (two HISTORYs of the same regulation), and scope (at the article level). It implicitly distinguishes from sibling tools like list_rule_history or get_rule_content by focusing on the comparison of two versions, making the purpose unmistakable.

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 the key optional parameter 'to_history_id' (if omitted, compares with the latest revision), which is essential for correct usage. However, it does not explicitly contrast with sibling tools or state when to prefer this tool over alternatives, leaving some implicit inference.

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?

Annotations already cover read-only/idempotent behavior; the description adds useful behavioral scope by indicating that only article/chapter content is returned and that tables are not included. It does not detail edge-case behavior like grep/head limits, but those are visible in the schema.

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 focused sentence plus one alternative pointer, with no redundant wording.

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, complete parameter schema, and annotations give enough context to call the tool correctly. It does not describe the return payload, but an output schema is indicated as present.

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?

All parameters are described in the input schema with examples, so the tool description adds no additional parameter-level meaning beyond the overall article/chapter scope.

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 states a specific use case: retrieve specific articles/chapters when LAW_ID is already known, and it explicitly points to lookup_dongguk_rule for tables/amount tables, distinguishing it from a key sibling tool.

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 gives clear when-to-use guidance (known LAW_ID, specific article/chapter view) and explicit when-not/alternative guidance for schedules and amount tables with lookup_dongguk_rule.

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?

The annotations already declare read-only and idempotent behavior, and the description adds no conflicting or misleading information. It provides a hint that the operation is fast and focused, which is consistent with the annotation.

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 with no unnecessary words or repetition. It efficiently conveys the tool's core function.

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?

While an output schema exists (so return values are presumably covered), the description omits any parameter context. The overall picture is mostly complete for the intended use, but the missing parameter details prevent full 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?

The schema has two parameters (law_id, history_id) but the description gives zero explanation of their meaning or usage. Since schema description coverage is 0%, the description fails to compensate, leaving the agent to infer from names alone.

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 table of contents of a regulation, specifying the structure (chapters, sections, articles). It is distinct from sibling tools that handle content, search, history, or comparisons.

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 phrase 'quick lookup' implies the appropriate use case—retrieving the TOC rather than detailed content or deep search. While not explicitly comparing to alternatives, the tool's purpose is unambiguous enough to guide selection.

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?

The description indicates a read-only operation ('확인합니다' - check), consistent with the readOnlyHint annotation. It describes what the tool provides without ambiguity.

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?

A single concise sentence covers the tool's function without redundancy or unnecessary detail.

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 tool with no parameters and simple informational output, the description adequately covers the scope. It could mention return format, but the provided information is sufficient for basic 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?

No parameters are defined, so the dimension is trivially satisfied. The description indicates that no input is needed, which is consistent with the empty parameter 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 that the tool checks Dongguk regulation MCP version, common/internal function scope, campus search limitations, and input guidance, giving a specific purpose.

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 lacks explicit guidance on when to use this tool versus the sibling tools, and does not mention any prerequisites or alternatives.

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?

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint, which cover the main behavioral aspects. The description adds no further behavioral details, but given the annotations provide solid transparency, a score of 3 is appropriate.

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 exceptionally concise, consisting of a single clear sentence in Korean. It avoids any unnecessary words or redundancy, making it easy to parse and understand.

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 provides the core purpose and the required parameter, but it lacks context about the output format, any sorting or filtering options, or how the history is structured. Given the simplicity of the operation, it is adequate but not highly comprehensive.

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 describes the single parameter 'law_id' with the description 'LAW_ID', which merely repeats the name and gives no additional semantic meaning. The tool description also does not elaborate on the parameter. Schema coverage is 100%, so the baseline is 3, but the lack of meaningful description prevents a higher score.

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 lists regulation amendment history, using the specific verb '조회' (inquiry/list) and resource '규정 개정 연혁' (regulation amendment history). It distinguishes from siblings like 'get_rule_content' or 'compare_rule_versions' by focusing on history, though it could be slightly more explicit about what the history contains.

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 does not provide any guidance on when to use this tool versus its siblings. It only states what it does, not when it is appropriate. Since there are multiple similar rule-related tools, explicit usage conditions would be beneficial.

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?

Annotations declare readOnlyHint and idempotentHint, and the description mentions no side effects or destructive actions, so it is consistent. However, it does not add extra behavioral details beyond the annotations, which is acceptable given the lower bar for annotated tools.

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, consisting of two sentences that clearly convey the tool's purpose and usage guidance. It is well-structured and avoids unnecessary detail, making it easy for an agent to grasp quickly.

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 output schema is available and the description mentions returning articles and annexes (including amount tables), the core behavior is covered. It also includes guidance on when to perform additional checks, making it fairly complete for the tool's complexity.

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?

All 9 parameters have schema descriptions with 100% coverage, including defaults and examples. The tool description itself does not add further meaning to parameters, but the existing schema documentation is sufficient, so a baseline score 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?

Description clearly states it is the primary tool for Dongguk University regulation questions, taking natural language queries or keywords and returning search results with original text and annexes. The verb 'return' and resource specification make the purpose unambiguous, distinguishing it as a high-level search tool.

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?

Provides explicit guidance on when to skip duplicate queries ('if basis is sufficient, omit duplicate queries') and when additional verification is needed (partial, ambiguous candidates, or unconfirmed applicability). This gives clear usage context, though it doesn't explicitly name sibling alternatives.

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?

Annotations already cover readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is clear. The description adds that it is an auxiliary tool returning only candidate lists, which is helpful, but it does not disclose any additional behavior such as pagination, result ordering, or limitations of the search. With annotations covering the main traits, a 3 is fair.

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 extremely concise—two short sentences that front-load the core purpose and usage guidance. There is no redundant phrasing or unnecessary detail, and 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 search tool with an output schema present, the description is largely complete: it covers the tool's role, its primary use case, and its relationship to the main lookup tool. It does not mention the sibling search_rule_deep, but that is not essential given the clear scope. The presence of an output schema also relieves the description from explaining return values.

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 description does not mention any parameters at all. The schema provides descriptions for campus, keyword, and full_text (60% coverage), but limit and offset are undocumented in both the schema and the description. Since the description adds zero parameter-level meaning and only partially compensates for the coverage gap, a 2 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 states a specific purpose: it returns a list of rule candidates only, and explicitly contrasts itself with lookup_dongguk_rule for general rule questions. This clearly distinguishes it from the main lookup tool and from other siblings like search_rule_deep by implying it is the lightweight candidate-search variant.

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 gives an explicit when-to-use: '규정 후보 목록만 필요할 때' (when only a candidate list is needed) and a direct alternative: 'lookup_dongguk_rule을 먼저 사용하세요' (use lookup_dongguk_rule first for general questions). This provides both positive and negative usage 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?

The annotations already declare readOnlyHint, openWorldHint, and idempotentHint, covering the behavioral traits. The description adds no extra behavioral context beyond these, so it meets the baseline but does not exceed it.

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, using two short sentences to convey the purpose and usage conditions without any unnecessary information or repetition.

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 provides enough context to differentiate this tool from siblings like search_rule and lookup_dongguk_rule by highlighting the 'full text' and 'multiple regulations' aspects. It does not mention output format, but that is not required for basic usage guidance.

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 provides no descriptions for the parameters (query, top, per_doc), and the description does not explain their meanings or expected formats. There is essentially no guidance on how to use these parameters, leaving the agent to guess.

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: searching the full text of multiple regulations. It also distinguishes itself from lookup_dongguk_rule, which is for single regulation questions, making the intent 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?

The description provides explicit usage conditions: use only when searching full text of multiple regulations, and advises using lookup_dongguk_rule for single regulation questions. This gives clear when-to-use and when-not-to-use 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?

Annotations already indicate read-only, idempotent, and open-world behavior, and the description adds concrete behavioral detail: it extracts rule/article/clause citations, checks existence, article title match, clause number, and explains campus scope handling.

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 compact and information-dense, with no redundant wording. It efficiently combines purpose, usage guidance, and behavioral details in two sentences plus clear parameter descriptions.

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 output schema exists, the description provides sufficient context: purpose, usage boundaries, parameter behavior, and relationship to sibling tools. It does not need to restate return values and covers the operational nuances relevant to 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%, and the descriptions add meaningful details: text has an explicit length limit and example, while campus explains its default, allowed values, and the behavior that campus is not an official filter and requires post-search scope verification.

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: verifying citations to Dongguk University regulations in documents/AI responses against the actual rulebook. It also distinguishes itself from lookup_dongguk_rule by explicitly directing content questions to that sibling tool.

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 explicitly says to use this tool for citation checks before approval and explicitly says to use lookup_dongguk_rule for rule content questions, providing clear when-to-use and when-not-to-use guidance.

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