Naver Search MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
The tools are clearly organized into two distinct groups: datalab trend analysis tools and search tools for different Naver services. Within each group, tools are differentiated by specific parameters like age, device, gender, or service type, making them mostly distinct. However, some datalab tools might overlap slightly in purpose (e.g., datalab_shopping_keywords and datalab_shopping_keyword_by_* variants), which could cause minor confusion.
Naming Consistency5/5
The naming follows a highly consistent pattern: all tools use snake_case with a clear prefix (datalab_ or search_) followed by a descriptive suffix (e.g., _search, _by_age, _blog). This consistency makes it easy to understand the tool's purpose and group at a glance, with no deviations in style or structure.
Tool Count4/5
With 19 tools, the count is on the higher side but reasonable for a server covering both search and trend analysis across multiple Naver services. It provides comprehensive coverage without being overwhelming, though it might feel slightly heavy compared to simpler servers. The tools are well-scoped to the domain of Naver's ecosystem.
Completeness5/5
The tool set appears complete for the server's purpose of accessing Naver search and trend data. It covers a wide range of Naver services (e.g., Blog, News, Shopping, Academic) with dedicated search tools, and the datalab tools provide detailed trend analysis across various dimensions (age, device, gender, category). There are no obvious gaps in coverage for the stated domain.
Average 2.9/5 across 19 of 19 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 5 of 5 community issues answered or closed in the last 6 months
- 3 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
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 mentions 'trend analysis' but doesn't specify what the analysis entails (e.g., returns data points, charts, insights), whether it requires authentication, rate limits, or data freshness. For a tool with no annotations, 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two short phrases, front-loading the main purpose. The Korean translation adds clarity for Korean users without being redundant. However, it could be more structured by explicitly stating the tool's scope or output.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 tool with 4 parameters and complex input (keyword groups). It doesn't explain what the trend analysis returns (e.g., time-series data, visualizations) or behavioral aspects like permissions or limitations. For a data analysis tool, this leaves too much undefined.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 (startDate, endDate, timeUnit, keywordGroups) with descriptions. The description adds no additional meaning beyond the schema, such as explaining how keyword groups are used in analysis or date format nuances. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 trend analysis on Naver search keywords.' It specifies the verb ('perform trend analysis'), resource ('Naver search keywords'), and includes a Korean translation for clarity. However, it doesn't explicitly differentiate from sibling tools like 'search_blog' or 'search_news' which might also involve Naver search 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.
Usage Guidelines2/5
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 (e.g., 'search_blog', 'search_news', 'datalab_shopping_by_age'), there is no indication of whether this is for general search trends versus specific domains or demographics. Usage is implied by the name 'datalab_search' but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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. It states the tool performs 'trend analysis' but doesn't describe what that entails—whether it returns aggregated data, visualizations, or statistical insights. It also omits information about rate limits, authentication needs, or data freshness, which are critical for a data analysis tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core purpose. The Korean translation adds redundancy but may aid clarity for Korean-speaking users. No extraneous information is included, making it appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data analysis tool with 5 required parameters and no output schema, the description is insufficient. It doesn't explain the output format (e.g., time-series data, charts), data granularity, or how results are structured. Without annotations or output schema, users lack critical context to interpret results effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 meaning beyond implying 'trend analysis' involves the parameters, but doesn't explain how they interact (e.g., how ages affect trends over time). Baseline 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.
Purpose4/5
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 resource ('Naver Shopping by age'), with the Korean translation reinforcing the purpose. It distinguishes from some siblings (e.g., datalab_shopping_by_device) by specifying 'by age', but doesn't explicitly differentiate from datalab_shopping_keyword_by_age, which is a similar but distinct tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention sibling tools like datalab_shopping_by_gender or datalab_shopping_keyword_by_age, nor does it specify prerequisites or appropriate contexts for trend analysis versus other shopping-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'trend analysis' but doesn't specify what that entails (e.g., returns data, charts, insights), whether it's read-only or has side effects, or any constraints like rate limits or authentication needs. This is inadequate for a tool with 5 required parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the core purpose. The Korean translation adds redundancy but doesn't significantly detract. It could be more concise by omitting the translation, but it's still efficient with no wasted sentences.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 5 required parameters, no annotations, and no output schema, the description is incomplete. It lacks behavioral context (e.g., what the analysis returns, any limitations), usage guidance relative to siblings, and doesn't compensate for the absence of structured metadata. This leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 thoroughly. The description adds no parameter-specific information beyond implying device analysis, which is covered by the 'device' parameter's enum. 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.
Purpose4/5
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 by device' with a Korean translation. It specifies the verb ('perform trend analysis'), resource ('Naver Shopping'), and scope ('by device'), but doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_gender' beyond the device focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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. It doesn't mention sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_gender' for comparison, nor does it specify prerequisites or exclusions. The description only states what it does, not when it's appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 mentions 'trend analysis' but doesn't specify what data is returned (e.g., time-series metrics, aggregated statistics), whether it's a read-only operation, any rate limits, or authentication requirements. For a data analysis tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded with the core purpose in the first sentence. The Korean translation adds minimal redundancy. However, it could be slightly more structured by explicitly mentioning the required parameters or output format.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (5 required parameters, no output schema, no annotations), the description is minimally adequate. It states the purpose but lacks details on behavioral traits, output format, and usage context. With no output schema, the agent doesn't know what data to expect, which is a significant gap for a trend analysis tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters clearly documented in the schema (e.g., 'Category code', 'End date (yyyy-mm-dd)'). The description adds no additional parameter semantics beyond implying gender-based filtering. With high schema coverage, 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.
Purpose4/5
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 trend analysis on Naver Shopping by gender.' It specifies the verb ('perform trend analysis'), resource ('Naver Shopping'), and dimension ('by gender'). However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_by_device' beyond the gender focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'datalab_shopping_by_age' for age-based analysis or 'datalab_shopping_keyword_by_gender' for keyword-focused gender analysis. The agent must infer usage from the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'trend analysis' but doesn't disclose what this entails operationally - whether it's a read-only query, requires authentication, has rate limits, returns aggregated data, or what format the analysis takes. The Korean translation adds no behavioral information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately brief with two equivalent language versions. However, the Korean translation adds no new information and could be considered redundant. The core English statement is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a data analysis tool with 4 required parameters and no output schema, the description is insufficient. It doesn't explain what 'trend analysis' returns, what metrics are analyzed, or provide context about the Naver Shopping platform. Without annotations or output schema, users must guess about the tool's behavior and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain how categories relate to trend analysis, what the time unit affects, or provide examples of valid category inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 target resource ('Naver Shopping category'), making the purpose understandable. It distinguishes from some siblings like 'datalab_shopping_by_age' by specifying category-level analysis, though it doesn't explicitly differentiate from all similar tools like 'datalab_shopping_keywords'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling tools for shopping data analysis (e.g., by age, device, gender, keywords), there's no indication of when category trend analysis is preferred over other dimensions or keyword analysis.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 offers minimal information. It states the tool performs 'trend analysis' but doesn't describe what the analysis entails (e.g., returns data, visualizations, or insights), potential rate limits, authentication needs, or data freshness. For a tool with 6 required parameters and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in English with a Korean translation for clarity, front-loading the core purpose without unnecessary details. It avoids redundancy and wastes no words, making it easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 required parameters, no output schema, and no annotations), the description is incomplete. It lacks behavioral context (e.g., what the analysis returns, any side effects), usage differentiation from siblings, and doesn't compensate for the absence of output schema by describing expected results. For a data analysis tool with multiple inputs, this leaves significant gaps for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters well-documented in the schema (e.g., 'ages' as age groups with enum values, 'timeUnit' with enum options). The description adds no parameter-specific semantics beyond implying analysis involves 'keywords' and 'age', which the schema already covers via 'keyword' and 'ages' parameters. 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.
Purpose4/5
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 by age' with a Korean translation for clarity. It specifies the verb ('perform trend analysis'), resource ('Naver Shopping keywords'), and dimension ('by age'), making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_age' or 'datalab_shopping_keyword_by_gender', 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.
Usage Guidelines2/5
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 'datalab_shopping_by_age' (which might analyze shopping trends by age without keyword focus) or 'datalab_shopping_keyword_by_gender' (which does similar analysis by gender), leaving the agent to infer usage from names alone. There are no explicit when/when-not instructions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 it performs 'trend analysis' but doesn't describe what the analysis returns (e.g., time-series data, metrics), whether it's read-only or has side effects, rate limits, authentication needs, or error conditions. For a tool with 6 required parameters and no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two parallel sentences (English and Korean). Both sentences directly state the tool's purpose without unnecessary elaboration. However, the Korean translation adds redundancy rather than new information, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 6 required parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the trend analysis returns, how results are structured, or provide context about the Naver Shopping platform. With rich parameter requirements but poor behavioral disclosure, the description leaves too many gaps for effective tool use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 6 parameters with descriptions and enums. The description adds no additional parameter information beyond what's in the schema - it doesn't explain relationships between parameters (e.g., how keyword interacts with device) or provide usage examples. With complete schema coverage, baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 by device' with a Korean translation. It specifies the verb 'perform trend analysis' and resource 'Naver Shopping keywords by device', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_device' or 'datalab_shopping_keyword_by_age', which would require more specific distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 'datalab_shopping_by_device' (which might analyze shopping trends by device without keyword focus) or 'datalab_shopping_keyword_by_age' (which does keyword analysis by age instead of device). There's no context about appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. While 'trend analysis' implies a read-only operation, the description doesn't specify whether this requires authentication, has rate limits, returns time-series data, or what format the analysis takes. For a tool with 6 required parameters and no output schema, this leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two parallel statements (English and Korean) that convey the same essential information. While the Korean translation adds redundancy, it doesn't significantly detract from efficiency. The description is front-loaded with the core purpose and wastes no space on unnecessary details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 required parameters, trend analysis function) and lack of both annotations and output schema, the description is insufficiently complete. It doesn't explain what the analysis returns, how results are structured, or provide context about the Naver Shopping platform. The agent must rely entirely on the parameter schema without understanding the tool's output behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with all parameters clearly documented in the input schema. The description adds no parameter-specific information beyond what's already in the schema. According to scoring rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description, which applies here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 by gender' with a Korean translation. It specifies the verb 'perform trend analysis' and resource 'Naver Shopping keywords by gender', making the purpose understandable. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_by_gender' or 'datalab_shopping_keyword_by_age', which would require more specific scope details.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling tools in the datalab_shopping family (by_age, by_device, category, keywords), there's no indication of when gender-specific keyword analysis is appropriate versus other demographic or categorical analyses. The agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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 'trend analysis' which implies a read-only operation, but doesn't disclose any behavioral traits like authentication requirements, rate limits, data freshness, or what the analysis output contains. For a data analysis 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with a single English sentence and a Korean translation. It's front-loaded with the core purpose. However, the Korean translation adds redundancy without additional information, slightly reducing efficiency.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
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 multiple sibling tools, the description is incomplete. It doesn't explain what the trend analysis returns (e.g., metrics, time series data), how it differs from other shopping keyword tools, or any prerequisites. For a data analysis tool with rich parameter requirements, more context is needed to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 5 parameters with descriptions. The description doesn't add any parameter-specific information beyond what's in the schema. It mentions 'keywords' generally but doesn't explain the keyword array structure or how category codes work. Baseline 3 is appropriate when schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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' with a Korean translation for clarity. It specifies the action ('trend analysis'), resource ('Naver Shopping keywords'), and domain context. However, it doesn't explicitly differentiate from sibling tools like 'datalab_shopping_keyword_by_age' or 'datalab_shopping_category', which appear to be related keyword analysis tools with different breakdowns.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 multiple sibling tools like 'datalab_shopping_by_age', 'datalab_shopping_keyword_by_gender', and 'search_shop', there's no indication of whether this is the primary keyword analysis tool or how it differs from others that might also analyze shopping keywords with demographic filters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.

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