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

No annotations are present, so the description carries the full burden for behavioral disclosure. Yet it only states the action ('trend analysis') without mentioning expected output, read-only nature, rate limits, or any operational constraints, leaving significant ambiguity for the agent.

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 sentence, front-loads the primary verb, and contains no filler. It is concise and easy to parse, though it sacrifices informative content for brevity.

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?

With four required parameters and no output schema, the description is under-specified. It does not explain the return value structure, how results are grouped by timeUnit, or any practical use cases, leaving the agent with significant gaps in understanding.

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 provides 100% coverage with descriptions for all four parameters (startDate, endDate, timeUnit, keywordGroups), so the schema already defines their meaning. The description adds no extra parameter context, but the baseline of 3 is appropriate given the schema completeness.

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 names a clear verb ('Perform') and resource ('trend analysis on Naver search keywords'), which correctly distinguishes it from search_* and datalab_shopping_* sibling tools. However, it doesn't specify the nature of the trend (e.g., search volume over time), so it stops short of being fully specific.

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 when-to-use guidance or alternative recommendations. It fails to explain how datalab_search differs from the numerous sibling tools or under what circumstances it should be preferred.

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 of behavioral disclosure. It does not mention whether the operation is read-only, any authentication or rate-limit requirements, or what the output contains. The phrase 'trend analysis' only vaguely implies a data-returning operation.

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, front-loaded sentence that conveys the core purpose immediately. The parenthetical Korean translation is mildly redundant with the English but does not harm clarity or add meaningful length.

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?

With no output schema and no annotations, the description does not explain the return format or any additional constraints. However, the schema fully covers required parameters and the tool's purpose is simple, making the description minimally viable but not 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 input schema already documents all 5 parameters with descriptions and enums, giving 100% schema coverage. The description adds no parameter-level detail beyond the schema, so it meets the baseline but does not enhance parameter 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?

The description clearly states the action ('perform a trend analysis'), the resource ('Naver Shopping'), and the segmentation ('by age'), which distinguishes it from sibling tools like datalab_shopping_by_device or datalab_shopping_by_gender. The verb 'Perform' is generic, but the resource and scope 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?

The description does not provide any explicit guidance on when to use this tool versus alternatives. It lacks mentions of excluded cases, prerequisites, or sibling tools, leaving the agent to infer usage solely from the 'by age' phrase.

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 provided, the description carries the full burden of behavioral disclosure, but it only says 'trend analysis' without revealing what the output looks like, whether it is read-only, or any limitations. The agent is left blind to the actual behavior beyond a vague operation.

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 efficiently communicates the tool's action and scope. There is no filler, and the parenthetical Korean translation is helpful for multilingual users without adding bloat.

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 absence of an output schema and annotations, the description must explain what the trend analysis returns and how to interpret it, but it does not. The tool has 5 required parameters, and the minimal description is insufficient for an agent to fully anticipate the response.

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 provides descriptions for all 5 parameters, so baseline is 3. The description adds no extra meaning beyond the schema, and it does not explain parameter relationships or constraints beyond what is already documented.

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 performs a trend analysis on Naver Shopping broken down by device. The verb 'Perform' and scope 'by device' make it distinct from sibling tools like datalab_shopping_by_gender or datalab_shopping_by_age.

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 'by device' subtly implies use when device-specific trend analysis is needed, but the description gives no explicit when-to-use or when-not-to-use guidance and does not differentiate from keyword-level device tools such as datalab_shopping_keyword_by_device.

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 bears full responsibility for behavioral disclosure. It only mentions 'trend analysis,' which implies a read operation, but does not specify output format, data granularity, limitations, or any side effects. This is a minimal disclosure gap.

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 in English and Korean, with no wasted words. It lacks structural elements like bullet points, but it is appropriately sized for a simple tool.

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 tool has five required parameters, no output schema, and no annotations, the description should provide more context about the analysis results, return values, or use cases. The generic 'trend analysis' phrasing leaves significant gaps for an agent to know what to expect.

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% parameter coverage with descriptions for all five required fields. The description adds no additional meaning beyond the schema, 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 performs a trend analysis on Naver Shopping, specifically segmented by gender. The verb 'perform' and resource 'Naver Shopping trend analysis' are specific, and the 'by gender' modifier differentiates it from sibling tools like by_age or by_device.

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 alternatives. With many similar datalab_shopping_* siblings, the lack of any usage context or exclusions leaves the agent without direction for selecting this tool.

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 must disclose behavioral traits. It only says 'perform a trend analysis' and does not state whether the operation is read-only, what response format to expect, whether pagination exists, or any rate limits or authentication requirements. This is a substantial gap.

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 in English and Korean, with no filler or redundant content. It efficiently states the action and resource.

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 lack of output schema and annotations, the description is too thin. It does not explain return values, date range semantics, category code structure, or any edge cases. The tool has four required parameters and is a data analysis tool, so more context is needed for complete understanding.

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%, so the input schema already documents all four parameters, including the enum for timeUnit. The description itself adds no parameter-specific meaning, making baseline 3 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 performs trend analysis on Naver Shopping category, with the action 'perform' and resource 'Naver Shopping category.' The category focus distinguishes it from sibling device/gender/age/keyword tools, though it does not explicitly call out those differences.

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 purpose sentence implies the tool should be used for category-level Naver Shopping trend analysis, but there is no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives or why this tool is preferable to sibling datalab_shopping_by_device, by_gender, or by_age tools.

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 are absent, so the description must disclose behavioral traits, but it only states that it 'performs a trend analysis.' It does not specify what the output looks like (e.g., time-series data, aggregation method), whether authentication is required, or any rate limits or other constraints. For a data-fetching tool, this is a significant transparency gap.

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 is front-loaded with the primary action. The Korean translation in parentheses is a minor redundancy but does not detract from clarity. Every word earns its place, and there is no verbose or filler content.

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 tool has 6 required parameters, no output schema, and no annotations, the description is insufficiently complete. It does not explain the return format, what 'trend analysis' entails, or how the parameters influence the output. An agent would be left uncertain about the consequences of invoking this tool, especially compared to richer descriptions of similar datalab tools.

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 description coverage is 100%, providing detailed definitions for all six required parameters, including enum values for ages. The description adds no extra parameter semantics beyond echoing 'by age,' which corresponds to the 'ages' parameter. Therefore, a baseline score of 3 is appropriate, as the schema already carries the weight.

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 ('Perform a trend analysis') and the resource ('Naver Shopping keywords by age'), which is specific and distinguishes it from sibling tools like datalab_shopping_by_age (category-level) and datalab_shopping_keyword_by_device (device-level). The Korean translation reinforces the same meaning, leaving no ambiguity about the tool's 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?

There is no guidance on when to use this tool versus alternatives. It does not mention that this tool is appropriate for keyword-level trend analysis segmented by age, nor does it exclude cases where other datalab tools (e.g., by gender or device) would be better. The description offers no contextual cues for tool 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?

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It only states the tool performs a trend analysis, implying a read operation but not specifying return format, whether it is read-only, rate limits, or required authentication. This lack of detail is insufficient for an agent to anticipate behavior or output structure.

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 very concise, with a single English sentence and a redundant Korean translation. It is front-loaded and avoids unnecessary words. However, the Korean translation adds no unique value, so it is not maximally efficient, but it is still appropriately brief.

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 tool has 6 required parameters, no output schema, and no annotations, the description is incomplete. It does not explain return values, result structure, or how keyword-level trends differ from category-level trends. This lack of context makes it hard for an agent to fully understand the tool's behavior and output, even though the schema clarifies input.

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%, with all six parameters described in the input schema. The description adds no extra parameter semantics beyond what the schema already provides, such as the 'by device' qualifier which is already captured in the device enum. Baseline 3 is appropriate since the schema does the heavy lifting.

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 the action ('Perform a trend analysis') and the resource ('Naver Shopping keywords by device'), clearly identifying the core function. It does not explicitly distinguish from sibling tools like datalab_shopping_keywords or datalab_shopping_by_device, but the 'by device' qualifier provides some differentiation. The verb is specific and the resource is well defined.

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 offers no guidance on when to use this tool versus alternatives. It does not mention scenarios for which it is appropriate, nor does it contrast with siblings such as datalab_shopping_keyword_by_gender or datalab_shopping_by_device. There is no exclusions or contextual advice, leaving the agent to infer usage from the name alone.

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, but it only states the high-level action. It does not mention what the response contains, whether results are aggregated by time or gender, or any limitations such as date range constraints or data source quirks. The Korean translation adds no additional behavioral insight.

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 with no redundant words. It communicates the core purpose efficiently. The Korean parenthetical is optional localization but does not detract from conciseness.

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 has six required parameters and no output schema or annotations. The description alone provides only the high-level purpose, which is sufficient to guess what the tool does but not enough to understand how parameters interact or what the result will look like. Given the presence of many similar sibling tools, more context about this tool's specific role would be needed for 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?

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add any extra meaning beyond the schema, such as how gender values map to 'female'/'male' or how category codes are used. It meets the baseline for complete schema coverage but provides no further value.

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 function: 'Perform a trend analysis on Naver Shopping keywords by gender.' It names the specific resource (Naver Shopping keywords), the action (trend analysis), and the grouping dimension (gender). This distinguishes it from sibling tools like datalab_shopping_keyword_by_device and datalab_shopping_keyword_by_age.

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?

No guidance is provided on when to use this tool versus alternatives. The description does not mention exclusions, prerequisites, or comparison with siblings such as datalab_shopping_by_gender. The usage context must be inferred entirely from the tool name and schema.

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 provided, the description carries the full burden but only says 'Perform a trend analysis.' It does not disclose the return format, pagination, rate limits, or any operational constraints. The term 'analysis' implies read-only, but that is not explicit, and no behavioral details are given.

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 directly states the action and resource. The Korean translation is a minor addition that does not inflate the content. There is no superfluous 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?

This is a moderately complex tool with 5 required parameters, no output schema, and no annotations. The description fails to explain what the trend analysis returns or how parameters interact, leaving significant gaps for an agent to correctly invoke the 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?

The input schema provides full descriptions for all 5 parameters (100% coverage), so the baseline of 3 applies. The description adds no extra meaning beyond what the schema already documents; it merely states the overall purpose.

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 performs trend analysis on Naver Shopping keywords, using a specific verb ('Perform') and resource ('Naver Shopping keywords'). This distinguishes it from sibling tools that break down by device, gender, or age, making 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?

No guidance is provided on when to use this tool versus the numerous sibling tools (e.g., datalab_shopping_keyword_by_device). The description does not mention alternatives, exclusions, or context for when the general keyword trend tool is appropriate.

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 provided, the description carries full burden but offers minimal behavioral insight. It states it performs a search but doesn't disclose rate limits, authentication needs, pagination behavior, error conditions, or what the search results look like. For a search tool with zero annotation coverage, this is inadequate.

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 with just two brief phrases, front-loading the core purpose. Every word earns its place, and there's no redundant or verbose language. The Korean translation adds precision without bloating the text.

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 no annotations and no output schema, the description is incomplete for a search tool with 4 parameters. It doesn't explain what results to expect, how they're structured, or important behavioral aspects like rate limits or error handling. The high schema coverage helps but doesn't compensate for missing output and behavioral context.

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%, providing good documentation for all parameters. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 without compensating value. The Korean text doesn't clarify parameters further.

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 ('Perform a search') and target resource ('on Naver Academic'), with the Korean translation adding specificity. It distinguishes this tool from most siblings (e.g., search_blog, search_news) by specifying the academic domain, though it doesn't explicitly differentiate from all possible search tools.

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?

No guidance is provided on when to use this tool versus alternatives. The description doesn't mention when academic searches are appropriate compared to other search tools (like search_webkr or search_news) or the datalab_* tools, leaving the agent to infer usage context.

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 provided, the description carries full burden for behavioral disclosure. It states the basic action but doesn't describe what the search returns (blog posts, metadata, links), whether there are rate limits, authentication requirements, or how results are structured. For a search tool with no annotation coverage, this leaves significant gaps in understanding the tool's behavior.

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 with just two short phrases that convey the essential purpose. Both the English and Korean versions are front-loaded with the core functionality, and there's no wasted language or unnecessary elaboration.

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 search tool with no annotations and no output schema, the description is insufficient. It doesn't explain what kind of results to expect (blog posts, metadata, links), how results are formatted, or any limitations. The combination of missing behavioral context and lack of output information makes this description incomplete for effective tool 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?

Schema description coverage is 100%, so the schema already documents all 4 parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does all the parameter documentation work.

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 ('Perform a search') and target resource ('on Naver Blog'), with the Korean translation adding clarity for the specific platform. However, it doesn't distinguish this from sibling tools like search_news or search_webkr, which also perform searches on Naver platforms but for different content types.

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?

No guidance is provided about when to use this tool versus alternatives. The description doesn't mention what makes blog search unique compared to other search tools in the sibling list, nor does it specify prerequisites or typical use cases for blog content versus other content types.

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 of behavioral disclosure. It states the tool performs a search but doesn't describe what the search returns (e.g., book titles, authors, metadata), whether it's paginated, rate limits, authentication needs, or error handling. This leaves significant gaps in understanding the tool's behavior beyond the basic action.

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 with just one sentence and a parenthetical translation, making it front-loaded and efficient. Every word serves a purpose: it defines the action and resource clearly without any fluff or 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?

Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what the search returns (e.g., book results format), behavioral aspects like pagination or errors, or how it differs from sibling tools. For a search tool with no structured output information, more context is needed to be fully helpful.

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, providing details for all 4 parameters (query, display, sort, start). The description adds no additional parameter semantics beyond what's in the schema. According to the rules, with high schema coverage (>80%), the baseline score is 3 when no param info is in the description, which applies here.

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: 'Perform a search on Naver Book.' It specifies the action (search) and resource (Naver Book), and the Korean translation adds clarity. However, it doesn't explicitly differentiate from sibling tools like search_academic or search_news, which also perform searches on different Naver services.

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 alternatives. It doesn't mention sibling tools like search_academic or search_news, nor does it specify scenarios where book searches are preferred over other search types. The only implied usage is for searching books, but no explicit context or exclusions are provided.

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 mentions 'Perform a search' but doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, error handling, or what the search returns. For a search tool with no annotation coverage, this leaves significant gaps in understanding how it behaves.

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 with just one sentence plus a Korean translation, which is front-loaded and wastes no words. Every part earns its place by clearly stating the purpose without 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?

Given no annotations, no output schema, and a search tool with potential complexity (e.g., result format, limitations), the description is incomplete. It doesn't explain what the tool returns, any constraints, or how to interpret results, leaving the agent with insufficient context for effective 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?

Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional parameter semantics beyond what's in the schema, such as explaining the search scope or result format. Baseline 3 is appropriate when the schema does the heavy lifting.

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 verb ('Perform a search') and resource ('on Naver Encyclopedia'), making the purpose explicit. It distinguishes from siblings by specifying the encyclopedia domain, though it doesn't explicitly contrast with other search tools like search_blog or search_news beyond the resource name.

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 alternatives. With many sibling search tools (e.g., search_blog, search_news, search_academic), there is no indication of when encyclopedia searches are appropriate or what distinguishes this from other search types.

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 full burden. It only states what the tool does ('Perform a search') without disclosing behavioral traits like rate limits, authentication requirements, response format, pagination behavior, or error conditions. For a search tool with no annotation coverage, this is a significant gap.

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 with just two short phrases that directly state the tool's purpose. Every word earns its place, and the Korean translation adds value for users familiar with that language without being redundant.

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 search tool with 4 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (image URLs, metadata, thumbnails?), doesn't mention rate limits or authentication, and provides no context about the Naver Image service. The description should do more given the complexity and lack of structured information.

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%, so the schema already documents all 4 parameters thoroughly. The description adds no parameter information beyond what's in the schema. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 ('Perform a search') and resource ('Naver Image'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this image search tool from its many sibling search tools (like search_blog, search_news, etc.), which would require 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 no guidance on when to use this tool versus alternatives. With 17 sibling tools including many other search types (academic, blog, news, etc.), there's no indication of when image search is appropriate versus other search tools or the datalab_* tools.

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 provided, the description carries full burden for behavioral disclosure but offers minimal information. It doesn't mention rate limits, authentication requirements, pagination behavior (implied by 'display' and 'start' parameters but not explained), error conditions, or what format/search scope the results will have. The description only states what the tool does at a high level.

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 appropriately concise with two parallel statements (English and Korean) that directly state the tool's purpose. There's no wasted verbiage or unnecessary elaboration, though the dual-language approach might be slightly redundant for some users.

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 search tool with 4 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what KnowledgeiN is (a Q&A platform), what kind of content it searches, what the results look like, or any behavioral constraints. Given the complexity of search tools and lack of structured metadata, more context is needed.

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?

With 100% schema description coverage, the schema already fully documents all 4 parameters. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain typical query formats, what 'sim' vs 'date' sorting means in practice, or provide examples. The baseline score of 3 reflects adequate but minimal value addition.

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 verb ('Perform a search') and resource ('on Naver KnowledgeiN'), with a Korean translation adding clarity for that audience. However, it doesn't distinguish this tool from its many sibling search tools (e.g., search_blog, search_news, search_academic) which all perform searches on different Naver platforms.

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 specific search tool versus the 15+ sibling search tools listed. There's no mention of what makes KnowledgeiN unique (Q&A platform vs. blog/news searches) or when an agent should choose this over alternatives like search_webkr or search_academic.

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 of behavioral disclosure. It only states the basic action ('Perform a search') without mentioning any behavioral traits such as rate limits, authentication needs, error handling, or what the output looks like (e.g., format, pagination). For a search tool with no annotation coverage, this is a significant gap in transparency.

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 and front-loaded, consisting of a single clear sentence with a helpful parenthetical translation. There is no wasted text, and it efficiently communicates the core purpose without unnecessary details, making it easy to parse 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 complexity of a search tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral aspects (e.g., how results are returned, error cases), usage context compared to siblings, and any prerequisites or limitations. This makes it inadequate for an agent to fully understand how to invoke the tool effectively.

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%, so the schema already documents all parameters (query, display, sort, start) with descriptions and constraints. The description adds no additional parameter semantics beyond what's in the schema, such as examples or usage tips. This meets the baseline of 3, as the schema handles the heavy lifting, but the description doesn't compensate or enhance 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?

The description clearly states the tool's purpose: 'Perform a search on Naver Local' with the Korean translation for clarity. It specifies the verb 'search' and the resource 'Naver Local', making it understandable. However, it doesn't explicitly differentiate from sibling tools like search_blog or search_news, which also perform searches on Naver but on different platforms, leaving some ambiguity about when to choose this specific 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 provides no guidance on when to use this tool versus alternatives. With many sibling tools like search_blog, search_news, and search_shop, there is no indication of what makes Naver Local unique or when it should be preferred over other search tools. This lack of context makes it difficult for an agent to select the appropriate tool without additional information.

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 states it performs a search but doesn't disclose behavioral traits like rate limits, authentication needs, result format, or whether it's read-only or has side effects. This is a significant gap for a tool with no annotation coverage.

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 with just two short phrases, front-loading the essential action and resource. There's no wasted language, and the bilingual note adds clarity without verbosity, making it highly efficient.

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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the search returns, potential limitations, or how it differs from siblings. For a search tool with no structured output information, more context is needed to be fully helpful.

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%, so the schema fully documents all parameters. The description adds no additional parameter information beyond what's in the schema, resulting in the baseline score of 3 where the schema does the heavy lifting.

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 verb ('Perform a search') and resource ('on Naver News'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like search_blog or search_webkr, which perform similar searches on different platforms, so it doesn't reach the highest score.

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 alternatives. With many sibling tools like search_blog and search_webkr available, there's no indication of what makes Naver News unique or when it's the appropriate choice, leaving usage unclear.

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 full burden. It mentions 'Perform a search' but lacks behavioral details like rate limits, authentication requirements, pagination behavior, error handling, or what the search results look like. For a search tool with zero annotation coverage, this is a significant gap in transparency.

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 with just one sentence plus a parenthetical translation, front-loading the core purpose without any wasted words. Every element serves a clear purpose, making it efficient and well-structured.

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 no annotations and no output schema, the description is incomplete for a search tool. It doesn't explain what the tool returns (e.g., result format, fields), behavioral constraints, or how it differs from sibling tools. The high schema coverage helps with parameters, but overall context is lacking for effective agent 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?

Schema description coverage is 100%, so the schema fully documents all 4 parameters (query, display, sort, start) with descriptions and defaults. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline of 3 for high schema 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 the action ('Perform a search') and target resource ('Naver Shopping'), with the Korean translation adding specificity. However, it doesn't differentiate from sibling tools like search_blog or search_news that perform similar search operations on different platforms, missing explicit distinction.

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?

No guidance is provided on when to use this tool versus alternatives. With many sibling tools for different search types (e.g., search_blog, search_news, search_webkr), the description offers no context about appropriate use cases or exclusions, leaving the agent to infer based on tool names alone.

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 of behavioral disclosure. It states the tool performs a search, implying it's a read-only operation, but doesn't cover aspects like rate limits, authentication needs, error handling, or what the output looks like (e.g., result format, pagination). For a search tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

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 and front-loaded, consisting of a single sentence with a clear purpose statement and a helpful Korean translation. There's no wasted text, and it efficiently communicates the core function without unnecessary details, making it easy to scan and understand 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 complexity of a search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., search results format, error responses) or address behavioral aspects like performance or limitations. With 4 parameters and many sibling tools, more context is needed to guide effective use, leaving gaps in overall understanding.

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, with clear documentation for all four parameters (query, display, sort, start). The description doesn't add any additional meaning beyond what's in the schema, such as explaining the 'sim' and 'date' sort options further or providing usage examples. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

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: 'Perform a search on Naver Web Documents' with the Korean translation for clarity. It specifies the verb 'search' and the resource 'Naver Web Documents,' making it easy to understand what the tool does. However, it doesn't explicitly differentiate from siblings like 'search_blog' or 'search_news,' which are also search tools on the same platform.

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 alternatives. With many sibling tools like 'search_blog,' 'search_news,' and 'search_academic,' it's unclear what makes 'search_webkr' unique or when it should be chosen over others. There's no mention of context, exclusions, or specific use cases.

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