Naver Search MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation5/5
Each search_* tool targets a distinct Naver content type (news, blog, shop, etc.), and each datalab_* tool covers a specific trend analysis dimension. The descriptions clearly differentiate otherwise similar-looking datalab_shopping variants.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with a clear prefix (search_ or datalab_) followed by the target or dimension. This uniformity makes the tool set easy to predict and navigate.
Tool Count3/5
With 20 tools, the set is on the heavier side. While each tool maps to a distinct Naver API endpoint, the datalab_shopping family alone accounts for 7 tools, making the overall surface feel somewhat large for the server's scope.
Completeness4/5
The server covers the major Naver search categories and trend analysis dimensions, providing a robust set for most query needs. Minor gaps exist, such as missing some specialized search categories (e.g., movie, people), but these are not critical.
Average 3.1/5 across 20 of 20 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 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.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
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If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?
Without annotations, the description carries the full burden of disclosing behavior, but it only says 'Perform a search' with no information about pagination, result format, limits, or side effects. This is a significant gap for a search tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence, which is concise, but it includes a redundant Korean translation that adds no value for an AI agent. It is short but under-specifies the tool's behavior, so it is not an optimal use of space.
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?
With no annotations, no output schema, and a sparse description, the agent lacks critical context about what the search returns, how to paginate, and when to choose this tool. The schema covers parameter semantics but not the overall tool 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%, so all parameters (query, sort, start, display) are already documented in the schema. The description adds no parameter details, but the schema does the heavy lifting, making baseline 3 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 a search specifically on Naver Blog, identifying the action and resource. It distinguishes from sibling search tools by naming the content type, though it does not elaborate on scope or special options.
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 such as search_news or search_shop. The description only states what it does, not when it should be selected.
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 must disclose behavioral traits. It only says 'Perform a search,' which implies a read-only operation, but it fails to describe return format, pagination, permissions, or any other consequences. This leaves the agent with insufficient information about what the tool actually does beyond the action.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with a Korean translation, which is concise but overly terse. It lacks meaningful detail needed for correct tool usage, making it under-specified rather than efficiently informative. It has no unnecessary words, but it also fails to communicate anything beyond the bare minimum.
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?
With no output schema, no annotations, and a description that lacks details about results, pagination, or alternatives, the tool is not adequately contextualized. The many sibling tools create ambiguity that is not resolved, making the description incomplete for reliable selection and invocation.
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 covers all four parameters (query, sort, start, display) with descriptions, achieving 100% schema description coverage. The tool description adds no parameter-specific information, so the baseline of 3 applies.
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 the resource ('Naver News'), which is more specific than a tautology. However, the verb is generic and the description does not explicitly differentiate from sibling tools like search_blog or search_shop, aside from the resource name itself.
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?
There is no guidance on when to use this tool versus the many sibling search tools. No alternatives are mentioned, no exclusions or prerequisites are stated, and the description merely restates the function without providing any decision-making 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?
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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
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 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.
Conciseness4/5
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.
Completeness3/5
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.
Parameters3/5
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.
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'), 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.
Usage Guidelines2/5
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.
Behavior2/5
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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose4/5
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.
Usage Guidelines2/5
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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Perform a search' and omits any details about read-only nature, rate limits, pagination behavior, or result format, which is a significant gap for a search 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 sentence with no redundant wording, making it appropriately concise. However, it is extremely minimal and does not provide structure beyond the core statement, so it does not earn a 5.
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?
The description is too sparse given the large group of sibling search tools and the absence of an output schema. It does not explain what results to expect, how to choose it over alternatives, or any operational constraints. The schema covers parameters, but the description lacks essential 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?
The schema provides descriptions for all four parameters (100% coverage), so the description does not need to add parameter details. The description itself adds no parameter semantics beyond the schema, meriting the baseline score of 3.
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 a search on Naver Book, with a specific verb and resource. It distinguishes from siblings mainly through the resource name, but does not explicitly contrast with other search tools, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance is provided. The description does not mention alternatives, exclusions, or typical use cases, leaving the agent to 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?
With no annotations provided, the description carries the full behavioral disclosure burden, but it only states 'search' without revealing read-only behavior, result shape, pagination behavior, or Naver-specific quirks. This is insufficient for a tool that returns data.
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 sentence with no filler, repetition, or unnecessary structure. It front-loads the core action and is appropriately concise for the schema-rich context.
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?
Despite complete schema and no output schema, the description does not explain what results look like, how sorting/output count work in practice, or what network/API behavior to expect. The sibling search tools share the same shape, so this lack of contextual detail leaves the tool feeling incomplete.
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% and every parameter (query, sort, start, display) has a clear Korean description. The tool description adds no parameter-level detail, so the baseline of 3 is appropriate since the schema fully documents parameters.
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 as 'Perform a search on Naver Image' with a specific verb and resource, and the resource 'Image' differentiates it from sibling search_news, search_blog, etc. However, it omits any scope qualifiers (e.g., what is returned, pagination defaults), so it stops short of being fully explicit.
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 given for when to use this tool over sibling search tools; the description does not mention alternatives or exclusions. The reader must infer usage from the name and siblings list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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 action and conveys no behavioral traits such as sorting limits, pagination, output format, or side effects.
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 one clear sentence plus a parenthetical Korean translation. It is efficient, though it may omit information that the schema does not cover.
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?
The tool has no annotations and no output schema, placing the burden on the description to provide context. It fails to mention return value structure, sorting behaviors, or pagination details, leaving the agent with only schema parameter descriptions.
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 provides full descriptions for all four parameters (query, sort, start, display), achieving 100% schema description coverage. The description adds no additional parameter semantics, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Perform a search' and a clear resource 'Naver Local', distinguishing it from sibling search tools like search_news or search_blog. The inclusion of the Korean equivalent adds localization context without ambiguity.
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?
There is no guidance on when to use this tool versus the many sibling search tools. It does not mention that it is for local businesses or provide any alternative tool references.
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?
Annotations are absent, so the description must carry the burden. It only says 'Perform a search' and gives no information about side effects, rate limits, authentication, or result format. This is minimal transparency.
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 sentence with a Korean translation in parentheses. It is concise and front-loaded, but the Korean duplication adds little value, preventing a perfect score.
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 4 parameters, no output schema, and no annotations, the description is insufficient. It doesn't mention pagination, sorting behavior, or what results are returned, leaving the agent without adequate 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?
The input schema already describes all four parameters (query, sort, start, display) with 100% coverage. The description adds no additional parameter semantics, so the score is the baseline 3.
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 states 'Perform a search on Naver Shopping' which clearly identifies the verb (search) and resource (Naver Shopping). However, it does not explicitly contrast with sibling search tools, so it earns a 4 rather than 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?
No usage guidance is provided; there is no mention of when to use search_shop versus other search_* tools such as search_news or search_blog. The description only gives the basic operation.
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, the description carries the full burden of behavioral disclosure, but it only states the basic function. No mention of pagination, sorting behavior, result count, rate limits, or auth requirements. The schema provides parameter definitions but not operational 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 a single sentence, immediately front-loaded with the key verb and resource. It is appropriately concise, though it borders on under-specification by omitting any further detail.
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 absence of an output schema and annotations, the description should provide more context about what the search returns or how to use the parameters effectively. It does not, leaving the tool's complete behavior and output structure unspecified. For a simple request, it may suffice, but it is not complete.
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 schema already provides 100% coverage for all four parameters with clear descriptions (e.g., 'query' as '검색어', 'display' as '한 번에 가져올 결과 수'). The description adds no additional parameter meaning or usage context beyond what the schema already conveys, so the baseline score of 3 applies.
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 the resource ('Naver Web Documents'), which distinguishes it from sibling tools like search_news or search_blog. However, it could be more explicit about what 'Web Documents' encompasses, though it is otherwise unambiguous.
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 its many siblings (e.g., search_news, search_blog). The description only states what the tool does, not the contexts in which it is the appropriate choice.
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 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.
Conciseness4/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose5/5
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.
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 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.
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 only states the action without revealing any traits such as rate limits, pagination behavior, result format, or whether the operation is read-only. This is a significant gap for a search 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, concise sentence with no unnecessary words. It is front-loaded and to the point, though it lacks additional structure or useful qualifiers. It earns a high score for brevity without verbosity.
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?
This is a 4-parameter tool with no output schema and no annotations. The description only states the action, providing no context about expected responses, default behaviors, or how to effectively use the optional parameters. It is incomplete for an agent to rely on alone.
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% for all four parameters, so the schema already documents each one clearly. The description adds no extra meaning or context beyond what the schema provides, meeting the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Perform a search on Naver Encyclopedia,' which specifies the verb (search) and the resource (Naver Encyclopedia). This distinguishes it from sibling tools like search_news or search_blog, so the purpose is unambiguous.
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 information about when to use this tool versus alternatives, nor does it mention any exclusions or recommended contexts. The sibling list shows many search options, but no guidance is given for selecting this one.
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 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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
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 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.
Usage Guidelines3/5
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.
Behavior2/5
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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose5/5
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 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.
Usage Guidelines2/5
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.
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 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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose5/5
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, 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.
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 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, and the description does not disclose any behavioral details beyond performing a search. It lacks information about output format, pagination behavior, or any side effects, leaving the agent largely uninformed about what to expect.
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, front-loaded sentence in English and Korean. It is appropriately concise with no unnecessary words.
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?
The tool has no output schema and no annotations, and the description omits any information about the response structure or results. An agent would need to infer the output format, which is a significant gap for a search 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?
The input schema already provides descriptions for all four parameters, covering 100% of them. The description adds no parameter-specific meaning, so it does not improve upon the schema baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool performs a search on Naver Academic, specifying the exact resource and distinguishing it from sibling search tools that target other services (news, blog, shop, etc.). The verb 'perform a search' is explicit and the resource name is specific.
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 regarding when to use this tool versus alternatives. The description only states that it performs a search on Naver Academic, with no mention of use cases, exclusions, or relationships to other search 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, 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.
Conciseness5/5
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.
Completeness3/5
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.
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. 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.
Purpose5/5
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.
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 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.
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 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.
Conciseness5/5
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.
Completeness2/5
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.
Parameters3/5
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.
Purpose5/5
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.
Usage Guidelines3/5
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.
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. However, it only states that the tool performs a search, without mentioning whether it is read-only, requires authentication, has rate limits, or returns specific data. 'Search' implies a read operation, but no explicit behavioral traits are disclosed.
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, focused sentence that clearly conveys the tool's purpose without unnecessary verbosity. It also includes a Korean translation, which may aid localization. It is efficient but slightly under-specified, so a 4 is appropriate.
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 simple search tool with fully documented parameters, the description is minimally viable. It states the target resource, and the schema covers parameter specifics. However, it lacks guidance on when to use this tool versus siblings and does not describe any return behavior, leaving some contextual gaps.
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 provides descriptions for all four parameters (100% coverage), so the description does not need to explain them. The baseline is 3 because the schema already handles parameter documentation, and the description adds no additional semantics beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
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 the resource 'Naver Cafe Articles', which precisely identifies the tool's scope. This distinguishes it from sibling tools like search_news or search_blog, 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.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indirectly implies usage by naming the target resource (Naver Cafe Articles), but it does not explicitly state when to use this tool over alternatives or mention any exclusions. There is no direct reference to sibling search tools, leaving the agent to infer context from the resource 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 responsibility for behavioral disclosure. It only states 'Perform a search' without describing the output format, result structure, pagination behavior, or any other behavioral traits. This is minimal and leaves the agent without meaningful transparency beyond the obvious action.
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 sentence and thus highly concise. However, the Korean parenthetical is redundant with the English text and adds no new information, slightly reducing efficiency. Still, it is well-structured and front-loaded.
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?
For a simple search tool with full parameter schema coverage, the description is adequate but not complete. It does not describe the return values or result format, which is notable because no output schema exists. It also lacks any usage context beyond the basic action. Overall, it is a minimally viable description with clear gaps.
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 provides descriptions for all four parameters (query, sort, start, display) with 100% coverage. The description adds no parameter-specific information, so it relies entirely on the schema. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource pair: 'Perform a search on Naver KnowledgeiN.' This clearly distinguishes it from sibling tools like search_news or search_blog, which target different Naver services. The Korean parenthetical reinforces the target service without adding ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies this tool is for searching Naver KnowledgeiN specifically, which is evident from the resource name. However, it does not provide explicit alternatives or when-not-to-use guidance, though the distinct target resource makes such exclusions less critical.
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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