KTRENZ K-Data

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

With no annotations provided, the description carries the burden of disclosing behavior. It notes provenance ('Measured first-party by KTRENZ') and the data dimensions, but it does not explicitly state read-only behavior, absence of side effects, rate limits, or caching. There is no contradiction, but behavioral transparency is thin.

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 and every clause adds information: the reach dimensions, momentum window, and data source. It is appropriately front-loaded with the core purpose. Some jargon ('answer-time vs training', '7d momentum') is dense but not wasteful, so it remains efficient.

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

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

For a one-parameter read tool with no output schema, the description lists the main result categories and the momentum window, which is enough to understand what the tool returns at a high level. It does not define the jargon or specify return format, but for a simple slug-based lookup it is reasonably 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?

Schema description coverage is 100%: the slug parameter is fully described with type, source (search_kdata), and an example. The description adds no parameter-specific meaning beyond the schema, so the baseline of 3 applies.

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

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

The description names the exact resource (artist reach) and enumerates its measured components: AI answer engines with vendor citations and answer-time vs training, tracked releases, and external demand from Naver and YouTube. This clearly distinguishes it from sibling get_release_reach by being artist-level, and the title 'Artist reach map' reinforces the resource.

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

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

The description provides no explicit guidance on when to use this tool versus get_release_reach or get_trending, and it does not state any exclusions. The only usage hint is in the schema parameter description pointing to search_kdata for finding the slug, but the main description itself offers no decision 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, the description carries the burden of behavioral disclosure. It adds useful context (first-party measurement by KTRENZ, what is measured) but does not mention permissions, data freshness, pagination, or whether reads are side-effect-free. It is informative but not complete.

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?

Two sentences, front-loaded with the core purpose, no filler. The source qualifier 'Measured first-party by KTRENZ' earns its place by adding credibility and context.

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 is simple (one parameter, no output schema) and the description communicates what is measured and where it comes from. It lacks an explicit statement about return format or interpretation, but for a tool of this complexity the description is largely sufficient.

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%, and the schema already explains the slug parameter with examples. The description adds no new parameter-level detail, so the baseline 3 applies.

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

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

The description states a specific verb and resource: measuring AI-citation reach for a specific release, which differentiates it from artist-level or trending tools. However, it does not explicitly name sibling tools or state what it is not, so it misses the highest level of sibling differentiation.

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

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

Usage context is implied through 'specific release' versus likely artist-level siblings, but there are no explicit when-to-use/when-not-to-use instructions or alternative tool references. The agent must infer when to choose this over get_artist_reach or get_trending.

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

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

With no annotations, the description carries the burden. It discloses that the data sources are external to KTRENZ and explains the velocity metric's components, but it omits any mention of output format, pagination, rate limits, or potential edge cases. This is adequate for a simple read operation 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 concise sentences with the core purpose front-loaded. It defines velocity, lists its components, and emphasizes that rank is the signal, all without wasted words. The structure is efficient and easy to parse.

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

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

For a simple tool with one optional parameter and no output schema, the description covers the main purpose and ranking logic. However, it does not specify the output structure or any details about the returned artist objects, leaving some ambiguity for an agent about what fields to expect.

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

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

The only parameter, limit, is fully documented in the schema with range and default, so schema coverage is 100%. The description adds no extra meaning about the parameter beyond what the schema already provides, so a 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 returns top rising K-pop artists ranked by 7-day velocity, explicitly contrasting with standing popularity. It distinguishes itself from siblings by focusing on velocity rather than reach or search, making its purpose unmistakable.

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/velocity data and explicitly says 'not standing popularity', which hints at when not to use it, but it does not name alternative tools or provide explicit when-to-use conditions. The contrast is helpful but underdeveloped.

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

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns 'matches with slugs + reach-map URLs', which conveys a read-only, non-destructive behavior. However, it does not mention rate limits, error cases, or explicit side-effect declarations, leaving some ambiguity for a tool with no annotation support.

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?

Two sentences with no filler. The first sentence front-loads the action and output, and the second gives a direct follow-up instruction. Every word serves a purpose, making it highly efficient.

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

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

For a tool with only 2 parameters and no output schema, the description covers the core usage (search by name), the output (matches with slugs + URLs), and next steps (use slug with related tools). No additional information is needed for an agent to call it correctly.

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

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

Schema description coverage is 100%, so the description baseline is 3. The description adds only the notion of 'by name' which aligns with the 'query' parameter, but it does not provide additional semantic detail beyond what the schema already specifies. It does not mention the 'limit' parameter's behavior beyond the schema text.

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

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

The description states a specific verb ('Search'), resource ('KTRENZ K-pop / K-culture entities'), and scope ('by name'). It also mentions the output (matches with slugs + reach-map URLs), which distinguishes it from sibling tools like get_trending. The follow-up instruction to use the slug with get_artist_reach or get_release_reach further clarifies its role.

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 clearly instructs what to do with the results ('Use the slug with get_artist_reach or get_release_reach'), giving a concrete workflow. It does not explicitly state when not to use this tool or mention alternatives such as get_trending, but the context is clear enough for a search-by-name use case.

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