KTRENZ K-Data

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

With no annotations provided, the description must carry the full burden of disclosing behavior. It states that the data is 'Measured first-party by KTRENZ' and lists the metrics included, but it does not explicitly state that this is a read-only operation or describe any potential side effects, rate limits, or authentication requirements. It also fails to mention the output structure, though it implies a fetch operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise—two sentences—and front-loads the primary purpose. It packs significant detail into the first sentence without excessive verbosity. However, the second sentence adds a minor contextual note about measurement source, which is not strictly essential but provides useful info. Overall, efficient and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the scope of what the tool returns (AI answer engine citations, tracked releases, external demand, 7d momentum) but does not specify return format or any behavioral notes like read-only nature. Given the absence of an output schema and annotations, the description leaves some gaps in expectations, particularly around what data format to anticipate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema provides complete documentation for the single 'slug' parameter (100% coverage), including an example. The description does not add extra meaning beyond the schema, so the baseline of 3 applies; the schema already handles the semantic burden.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: it retrieves an artist's measured reach across AI answer engines, tracked releases, and external demand. It distinguishes itself from siblings like get_release_reach (focused on releases) and get_trending (likely trending items) by explicitly mentioning 'artist's' and the specific metrics covered, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for artist-related metrics and mentions that the slug comes from search_kdata, but it does not explicitly state when to prefer this over alternatives like get_release_reach or get_trending. No exclusions or alternative guidance is provided, so the context is implicit rather than explicit.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It usefully adds that the measurement is first-party by KTRENZ and spans answer engines, but it does not clarify what 'reach' means, whether the result is a single number or a breakdown, or any access/rate considerations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences with no filler. It front-loads the core purpose and then adds the measurement source, making it easy to scan and understand.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With one simple parameter and no nested objects, the tool is not complex, but there is no output schema and the description does not state the return format or how the reach metric is presented. The description is adequate for basic understanding but leaves some ambiguity about what the caller actually gets.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully describes the single slug parameter with an example, so the description does not need to add much. The tool description does reinforce that slug refers to release types like comebacks, which aligns with and slightly expands the schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly defines the tool as measuring AI-citation reach for a specific release, listing concrete release types (comeback, MV, tour, chart announcement). This distinguishes it from siblings like get_artist_reach, which targets artist-level reach, and get_trending/search_kdata.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description makes clear that the tool is for release-specific reach metrics, implying it should be used when you need data for a particular comeback/MV/tour/chart event rather than artist-level or trending data. It does not explicitly name alternatives or exclusions, but the release-scoped wording provides enough contextual guidance.

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

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

With no annotations provided, the description takes on the burden of disclosure. It explains the data sources (external to KTRENZ), what 'velocity' means, and that rank is the signal, providing useful behavioral context. However, it does not explicitly state that this is a read-only operation or discuss side effects or rate limits, which would push it to a 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero fluff. The key information (velocity metric, not popularity, external sources, rank as signal) is front-loaded and every sentence adds value. It is concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given there is no output schema, the description gives a good sense of what to expect (list of rising artists) and explains the ranking logic. It does not specify the exact response fields or pagination, but for a simple one-parameter tool, it is sufficiently complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully describes the 'limit' parameter (1–30, default 15). The description does not add any additional meaning about the parameter, so it meets the baseline of 3 for high schema coverage without adding extra value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns top rising K-pop artists ranked by 7-day velocity, explicitly distinguishing from standing popularity. It uses a specific verb and resource (get trending artists) and differentiates itself from sibling tools by emphasizing the velocity metric.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for trending/rising artists, and mentions it's not based on standing popularity, but it does not explicitly name alternative tools or provide when/when-not guidance. The distinction is implicit rather than direct.

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

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

With no annotations provided, the description carries full responsibility for behavioral disclosure. It states that the tool returns matches with slugs and reach-map URLs, which gives a basic picture, but it does not disclose matching behavior (exact/partial/fuzzy), pagination, or potential failure modes. This is acceptable for a simple search tool but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the core purpose and immediately useful downstream guidance. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has only 2 simple parameters and no output schema, and the description provides a clear enough output contract (matches with slugs and reach-map URLs). It also notes how results should be used. Missing details like return structure are not critical given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with query and limit already clearly documented in the input schema. The description adds minimal semantic value beyond mentioning name-based search, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches KTRENZ K-pop/K-culture entities by name and returns matches with slugs and reach-map URLs. It distinguishes itself from sibling tools like get_artist_reach, get_release_reach, and get_trending by explicitly framing itself as the lookup tool for slugs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear guidance on when to use the tool: searching by name to obtain a slug for later use with get_artist_reach or get_release_reach. It does not explicitly mention when not to use it or compare it to get_trending, but the intended workflow is strongly implied.

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