TubeSaku Creator Insights

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

With no annotations, the description carries the behavioral burden and discloses important traits: free-version caps, the default registrant segment, null handling for missing old aggregate values, and the distinction between Steam and YouTube metrics. It does not cover authentication, rate limits, or response shape, so it stops short of full transparency.

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

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

The description is four short sentences with the core purpose front-loaded. Each sentence adds relevant information about limits, defaults, null handling, or metric separation, with no wasted phrasing.

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 four-parameter tool with no annotations, no output schema, and zero schema description coverage, the description covers several key constraints but leaves the game parameter and return semantics under-specified. It is adequate but not complete enough to make the tool fully self-explanatory.

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 0%, so the description must compensate. It clarifies that weeks is capped at 4 and games at 5 on the free version, and that the default segment is registrants under 10,000, but it never explains the game parameter or enumerates valid segment values.

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 a specific resource and operation: weekly comparison of demand and supply rankings by game. It distinguishes the metrics indirectly by noting Steam player counts and YouTube views are separate, but it does not differentiate this tool from any sibling because none are directly comparable.

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 free-tier limits and default segment imply when the tool is usable, but there is no explicit when-to-use guidance, no alternatives named, and no when-not-to-use conditions. The constraints are useful but do not fully substitute for routing 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 full disclosure burden, and it does reveal what the agent will learn (staleness, missingness, usage/licensing conditions) — valuable behavioral context for a data-fetch workflow. However, it never states that the call is read-only/side-effect-free, nor anything about caching, auth, or the shape of the returned catalog. Adequate but incomplete for a tool with zero annotation coverage.

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

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

Two sentences, no filler, and the precondition (check freshness first) is placed after the content list but still clearly separated. The first sentence is a dense attribute list, which is efficient though slightly telegraphic.

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?

There is no output schema and no annotations, so the description must convey both return content and safety profile. It lists the metadata fields well but says nothing about the catalog's structure, whether it covers all datasets or only free ones, or the read-only nature of the call — meaningful gaps for a no-arg introspection tool.

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

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

The tool takes no parameters, so there is nothing to disambiguate; the baseline of 4 applies. The description's implicit note that the catalog covers "free data" is the only scoping information an agent might want.

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 resource (a catalog of free/available data) and enumerates what it exposes: update timestamps, observation range, missingness, staleness, and usage conditions. That is clearly more than a restatement of the name. It does not, however, distinguish itself from siblings like get_tubesaku_service_info or get_tubesaku_index, which plausibly surface overlapping metadata.

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?

"取得前に鮮度を確認してください" gives an explicit precondition: consult this catalog before pulling data to check freshness. That is real when-to-use guidance. It stops short of naming alternatives or stating when not to use it (e.g., versus service_info).

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 present, so the description carries the full disclosure burden. It identifies the source (llms.txt) and output (public Markdown list), but does not state that the call is read-only, whether it makes a network request, or how results are formatted.

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?

A single front-loaded sentence that states source and return value with no filler. It is appropriately sized for a zero-parameter index tool.

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

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

With no parameters and no output schema, the description gives the minimum needed to understand the call (read llms.txt, return a public Markdown list). However, it does not clarify the return format or explain its relationship to the similarly named data catalog and markdown-reading siblings.

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 tool has zero parameters, so there are no parameter semantics to explain; the baseline score for no params is 4. The description neither needs nor provides param details.

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?

States a specific verb (reads) and resource (TubeSaku's llms.txt) plus the output (list of available public Markdown). It does not name or distinguish sibling tools like get_tubesaku_data_catalog or read_tubesaku_markdown, so sibling differentiation is missing.

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

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

No explicit when-to-use or when-not-to-use guidance. The purpose implies it is for discovering public Markdown files, but it never says to prefer it over get_tubesaku_data_catalog or read_tubesaku_markdown, leaving alternatives unaddressed.

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 behavioral burden. It implies a read-only information lookup and describes the content areas returned, but it does not explicitly state read-only behavior, permissions, side effects, or response format.

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

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

The description is a single efficiently written sentence with no filler. It is appropriately sized for a simple zero-parameter info tool, though it leads with content items rather than an explicit action framing.

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 zero-parameter service-info tool with no output schema and no annotations, the description should clarify what the agent will receive. It names three content areas but leaves “提供状況” ambiguous and does not say whether pricing, contact details, or terms are included, so it is adequate but not fully 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 tool takes zero parameters and the schema is an empty object with 100% description coverage, so there are no parameter semantics to explain. The rubric baseline for zero parameters is 4.

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 retrieval purpose: it returns the scope of free data, information about the paid individual analysis/report consultation desk, and availability status. This is clearly more than a tautology and distinguishes it in substance from data-catalog or search siblings, though it does not explicitly name those alternatives.

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

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

There is no explicit guidance on when to use this tool versus the sibling tools such as get_tubesaku_data_catalog, get_tubesaku_index, or search_tubesaku_context. Usage is only implied by the tool name and the returned-content list.

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 behavioral burden. It does not say what happens with an invalid path, whether the file content is returned whole or truncated, or how large documents are handled; only the read intent and source (llms.txt) are conveyed.

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 short lines: purpose first, example second. No filler, and the concrete path example is placed where an agent will need it immediately.

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 single-parameter read tool with no output schema this is minimal but workable; the path example covers the main ambiguity. It still lacks any indication of return shape or the relationship to the index/catalog tools that presumably enumerate valid paths.

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 0% and the single parameter has no description, so the description must compensate. The example '/llms/google_trends.md' supplies real format meaning — leading slash, directory prefix, .md extension — that the schema alone does not provide.

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?

States a specific verb (read) and resource (TubeSaku Markdown files published via llms.txt), which separates it from siblings like get_tubesaku_index or get_tubesaku_data_catalog. It does not explicitly name those siblings, so differentiation is inferable rather than stated.

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 use case (fetching a specific Markdown document) and the path example suggests the input should look like a published file path. There is no explicit guidance on when to prefer this over get_tubesaku_index or search_tubesaku_context, nor on whether the path must come from the index.

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 behavioral burden. It does reveal useful context, namely that results are ranked by three named signals, that the output is capped at five songs, and that music licensing must be verified separately. It still leaves unstated the return shape, whether keyword filtering interacts with scope, and what happens when fewer than `limit` candidates exist.

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 three compact sentences, with the action and ranking criteria front-loaded, followed by the scope list and the licensing caveat. Nothing is wasted, though the bare scope enumeration could be tightened with a few words about what each mode targets.

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 4 parameters, zero schema coverage, no annotations, and no output schema, the description has to do most of the work. It covers the purpose, the ranking signals, the scope enum, and the limit cap, but leaves both remaining parameters and the returned fields undocumented, so an agent still has to guess.

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

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

Schema coverage is 0%, so the description is the only source of parameter meaning. It documents the five valid `scope` strings, which the schema omits, and implies a hard cap of five on `limit`. It says nothing about `keyword` matching behavior or what `require_playlist_video` filters, leaving half the parameters unexplained.

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 first sentence names a specific verb (探す) and resource (歌ってみた候補) and even explains the ranking inputs: original-song popularity, recent competitor count, and reaction at roughly six hours after release. That distinguishes it from siblings like search_live_streams or get_game_trends. A related sibling, search_tubesaku_context, could overlap, but the description never states how the two differ.

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 five scope values suggest the intended situations (low_supply, no_recent_covers, shorts_growth, popular_covers, all), so the agent gets an implicit sense of when each one helps. However, the description never says when to choose this tool over the sibling search tools, gives no prerequisites, and explains none of the modes.

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

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

With no annotations, the description carries the full behavioral burden and does add useful constraints: limited free preview (7 days / 10 items) and no postponement/cancellation history. It still omits return format, pagination behavior, authentication needs, and whether results are ordered or truncated in other ways.

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 three short sentences with no filler. The main search capability is front-loaded and the availability caveats follow compactly.

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 an 8-parameter search tool with no output schema, the description covers the core purpose and important availability limits. It remains incomplete on parameter formats and return-value behavior, which an agent would need in order to invoke it precisely.

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 0% across 8 parameters, so the description must compensate. It conceptually covers date, time range, debut-only, and genre, but omits the keyword and limit parameters and provides no format guidance for date/time strings or what 'debut_only' means in practice.

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 gives a specific verb and resource ('配信予定を...検索') and names the filter dimensions: JST date, time range, debut candidates, and genre. It does not explicitly differentiate from sibling tools, but none of the siblings are similar search tools, so the purpose remains clear.

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 when the tool is useful by listing searchable dimensions and disclosing that free previews are limited to 7 days and 10 items, and that postponement/cancellation history is unavailable. However, it does not name alternative tools or state explicit when-not-to-use conditions beyond those availability caveats.

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 must carry the behavior burden. It does convey that this is a read-only operation over public Markdown and that matching is approximate ('関係しそうな' - likely related) rather than exact, which is genuinely useful. It omits result limits, whether snippets or full documents are returned, and how multiple keywords or phrases are handled.

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 short sentences, front-loaded with the operation and resource, followed by a useful scoping note that this is the simple keyword variant. No filler.

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 search tool with no output schema, the agent needs to know roughly what comes back. The description says related body text is returned but not how much, in what form (snippet vs. whole document), or with what metadata, leaving a meaningful gap.

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

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

Schema coverage is 0% and the single parameter has no description, so the description must compensate. 'queryに関係しそうな本文' establishes that query is a relevance-bearing search string, but adds no detail on tokenization, multiple keywords, phrase syntax, or operators.

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?

States a specific verb and resource: reads across TubeSaku's public Markdown and returns body text relevant to the query. It also signals this is the 'simple keyword search version', hinting at a tier relative to other retrieval tools, but it never names a sibling such as read_tubesaku_markdown or get_tubesaku_index, so differentiation is left to inference.

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

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

The phrase 'まずは単純なキーワード検索版' implies this is the entry-level retrieval option, which is a usable usage cue. However, there is no explicit when-to-use/when-not guidance and no mention of when to prefer the sibling read or index tools, so the agent must guess at routing.

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