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

With no annotations, the description carries the full burden, and it does disclose core behavior: bulk collection, deduplication, optional ItemLookUp details, and that files are only saved when export_dir is set. However, it does not describe what the tool returns, how it handles errors or rate limits, whether files are overwritten, or what happens when export_dir is null beyond not saving.

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 front-loaded sentence states the tool's overall function, followed by a compact parameter list where every entry adds meaning. There is no filler or redundant narrative, and the structure is easy for an agent to scan.

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 an 8-parameter tool with no output schema and no annotations, the description covers parameters and the main workflow well. Still, it omits what the tool returns to the caller, overwrite or append behavior for exported files, and any API-side considerations such as rate limits or required credentials, leaving notable gaps for an agent deciding whether the call succeeded.

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 0%, but the Args section compensates fully by explaining all 8 parameters with concrete examples and defaults, including the meaning of opt_lookup and the conditional behavior of export_dir. This is substantially more useful than the bare schema titles and defaults.

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 begins with a specific, multi-part behavior: collecting books across multiple search terms, deduplicating results, and exporting to xlsx/csv/json/sqlite. This clearly distinguishes it from the sibling search/lookup/status/list tools, which are single-query or detail-oriented rather than bulk export operations.

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 makes the intended use case clear: bulk collection across multiple terms with deduplication and file export. It does not explicitly state when to prefer aladin_book_search or aladin_book_lookup instead, but the batch-and-export framing gives an agent enough context to infer the right situation.

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 full burden of behavioral disclosure. It uses '조회합니다' (queries), implying a read-only operation with no side effects, and it documents pagination limits (start 1~100, max_results 1~100). However, it does not disclose auth requirements, rate limits, error behavior, or response format. It provides some useful behavioral constraints but is 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 efficiently structured: a one-sentence purpose statement front-loads the core function, followed by a compact 'Args' block that spends each line on a parameter with its allowed values or range. There is no fluff, repetition of schema defaults, or irrelevant detail. 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?

For a 5-parameter tool with no annotations and no output schema, the description covers parameters very well but omits return-value behavior and usage guidance. An agent knows what to pass, but not what the response contains or how to decide between this and aladin_book_search/lookup. Given the lack of an output schema, a note about the return structure would materially improve 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 0% description coverage, so the description must compensate, and it does thoroughly. The 'Args' section explains each of the 5 parameters: query_type lists the five allowed values, search_target lists the seven categories, start and max_results give ranges, and category_id is marked as optional. This adds significant meaning beyond the bare schema, which lacks enums, ranges, and 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 opens with a clear verb and resource: '알라딘 베스트셀러, 신간, 추천도서 등 큐레이션 리스트를 조회합니다' (queries curated lists such as bestsellers, new arrivals, and recommended books). This states a specific retrieval action and resource. It does not explicitly name sibling tools or contrast with them, so it falls short of full differentiation, but the focus on curated lists is distinct from search, lookup, status, and collect.

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 no guidance on when to use this tool versus the sibling tools. It does not mention 'use this for list queries' or 'use search instead for keyword queries.' The only implied usage is from the purpose sentence, but there are no exclusions, prerequisites, or alternative routing. An agent must infer selection solely from the tool name and examples, which is not sufficient.

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 full burden. It discloses the read-only nature ('조회'), accepted identifier formats, and auto-detection of item_id_type. However, it does not describe return structure, error behavior, or the inconsistency between the schema default (ISBN13) and the claimed auto-detection.

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 and well-structured, with a front-loaded purpose statement followed by a compact Args list. There is no filler, though the auto-detection/default inconsistency and the cryptic opt_result value list prevent a perfect score.

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 straightforward lookup tool, it covers purpose, identifier types, optional results, and returned fields reasonably well. But with no annotations and no output schema, an agent still lacks expected response shape, failure conditions, and a concrete example; the item_id_type ambiguity adds further uncertainty.

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 Args section meaningfully compensates by explaining all three parameters: item_id formats, item_id_type values, and opt_result purpose with defaults. It does not expand on each opt_result value, but the default string is informative. The ISBN13 default versus auto-detection discrepancy reduces clarity slightly.

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 action ('조회합니다') and resource ('특정 도서' identified by ISBN13 or 상품ID), and lists the returned attributes. It is distinguishable from search/list/status as an identifier-based single-item lookup, though it does not name sibling tools explicitly.

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?

Usage is implied: use this when you have an ISBN or ItemId and need detailed information about a specific book. However, it does not explicitly say when not to use it or how it compares with siblings like aladin_book_search or aladin_book_status.

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?

There are no annotations, so the description carries the burden of disclosing behavior. It only says the tool searches books and lists parameters; it does not disclose return format, output shape, API limits, or whether any side effects exist. The word 'search' implies a read-only operation, but that is not explicitly stated.

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: one clear purpose sentence followed by a compact argument list. Every line earns its place, and the purpose is front-loaded before the parameter details.

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?

Input-side documentation is complete for all seven parameters, which is valuable for a tool with no output schema. However, because there is no output schema and no annotations, the description should also explain what the tool returns and how results are structured; this is missing.

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%, and the description compensates fully by explaining all seven parameters with semantic meaning and allowed values. It adds useful constraints like page range, result count range, and valid enum-like options that are absent from 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 action and resource: searching books via the Aladin Open API. It is unambiguous about the tool's basic function, though it does not explicitly differentiate this search tool from the sibling tools by name or condition.

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 no guidance on when to use this tool versus the sibling tools such as aladin_book_lookup, aladin_book_list, or aladin_book_status. Usage context is only implied by the tool name and the 'search' verb, with no exclusions or alternative routing.

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 transparency burden. It does disclose a meaningful behavioral trait: the tool performs one actual network round-trip to the Aladdin Open API, which implies external dependency and possible latency. However, it does not explicitly state that the operation is read-only, what it returns, or how errors are signaled.

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, front-loaded sentence that names the main purpose first and then adds the round-trip detail. Every part contributes meaning, with no filler or repetition of the tool name.

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 zero-parameter health-check tool, the description is adequate but not complete: it explains what is being checked but says nothing about the output/return value, failure behavior, or how an agent should interpret the status result. Since there is no output schema and no annotations, some of that context is missing.

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 zero properties and the description references no parameters, so there is no param-semantic gap for the description to fill. With 0 parameters, the baseline is 4, and the description does not need to add parameter-level detail.

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 checks two specific things: the authentication key status and a real round-trip connection to the Aladdin Open API. This is a specific verb-resource pairing and inherently distinguishes it from sibling tools like aladin_book_search, aladin_book_lookup, aladin_book_list, and aladin_book_collect, which are all data operations rather than health checks.

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 no explicit guidance on when to use this tool versus the sibling tools. It does not say 'run this before other Aladdin calls' or 'use when the auth key may be invalid,' so an agent has to infer the usage context from the word 'status' alone.

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