tubemind-secure-mcp

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

No annotations are provided, so the description must carry the transparency burden. It states the tool analyzes and generates recommendations, implying a read-only analysis, but does not disclose output format, data sources beyond 'top videos', or any potential side effects. This is moderate 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 two sentences and efficiently packs the purpose, the proxies analyzed, the patterns identified, and the output type. Every word contributes value with no repetition or 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?

There is no output schema, so the description should convey what the recommendations look like. It says 'gera recomendações de roteiro' but does not elaborate on structure, format, or how to consume the output. Given the clear purpose and documented parameters, it's adequate but incomplete.

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 100%, so the baseline is 3. The description mentions the retention proxies but these are analysis outputs, not parameters. It adds minimal meaning beyond the schema's parameter descriptions, so no credit above baseline.

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 analyzes retention proxies (engagement rate, amplification, duration, chapters) of top niche videos and generates script recommendations. This specific verb+resource separates it from sibling tools like analyze_title_patterns or benchmark_channel.

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 using this tool when you need to understand retention patterns and generate script recommendations for a niche. It does not explicitly contrast with alternatives or state when not to use it, but the context is clear enough for an agent to select it appropriately.

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 full behavioral burden. It explains the analytical behavior and output (templates), but does not define 'most successful', mention data sources, side effects, or limitations. The verb 'analisa' implies read-only, but this is not explicit.

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 primary action, and each clause adds relevant detail. It is concise and well-structured, with no redundant information.

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 output schema, the description needs to explain return value structure, but it only vaguely mentions returning templates. It does not specify the format, count, or how 'successful' is measured, leaving gaps for an agent to know exactly what to expect from the 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?

Schema description coverage is 100% so the baseline is 3. The description adds no additional meaning beyond the schema's parameter descriptions for niche, regionCode, and videoCount. It does not clarify parameter usage or constraints beyond what is already in the schema.

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 analyzes structural patterns of successful video titles in a niche, identifies recurring formulas (number, question, how-to, contrast, urgency), and returns dominating templates. This is a specific verb+resource and distinguishes it from sibling tools like keyword stats or tag suggestions.

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 context for when to use it: when needing to understand title patterns in a niche. It does not explicitly exclude alternatives or mention related tools like suggest_hook_angles, so it falls short of explicit when/not guidance but is still clear enough.

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 burden. It mentions the authenticated channel and details the return output, which adds useful context. However, it doesn't explicitly state that the operation is read-only or describe any limitations, data freshness, or error handling, leaving some behavioral aspects undisclosed.

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, front-loaded sentences. The first states the core action, the second lists output components, and the third explains the comparative insight. Every sentence adds value with no redundancy or fluff.

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 parameter and no output schema, the description adequately covers input constraints and output content. It names the metrics and the comparison outcome. It could mention edge behaviors like invalid IDs or data availability, but overall it is sufficiently complete for 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?

The schema fully documents the single parameter (competitorChannelIds) with description, format, and min/max. The tool description reinforces the 1-3 channel constraint but adds no new semantic detail beyond what the schema already provides, 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 clearly states a specific action ('Compara seu canal autenticado') with a defined resource (own channel vs. competitors) and scope (1 to 3 channels). It also lists the returned metrics, distinguishing it from sibling tools that focus on individual channel analytics or competitor videos.

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 usage context is implied ('compares your channel with competitors') but the description provides no explicit guidance on when to choose this tool over alternatives like get_channel_analytics or get_competitor_videos. There are no exclusions or prerequisites mentioned.

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?

Sem annotations, a descrição carrega o peso da transparência. Ela revela que o tool classifica subtópicos em três categorias, o que é um comportamento útil. Porém, não informa o formato de retorno, se há necessidade de autenticação, ou como os resultados são ordenados, deixando lacunas para o agente.

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 descrição é curta (duas frases) e direta, sem informações redundantes. A primeira frase define o propósito, e a segunda acrescenta a classificação e a referência ao VidIQ, tudo sem desperdício.

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?

Para uma ferramenta com 3 parâmetros e sem output schema, a descrição dá o contexto central e um exemplo de classificação, mas não detalha o formato do retorno (ex: lista ordenada, métricas). A ausência de annotations e output schema torna a descrição apenas mediana em completude.

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?

O schema cobre 100% dos parâmetros com descrições claras, então a baseline é 3. A descrição não adiciona significado extra além de mencionar 'nicho' e 'criadores', que já está implícito no schema. O papel do yourChannelId é explicado no schema, não na descrição.

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?

A descrição usa verbo específico 'Identifica' e recurso 'subtópicos do nicho', definindo claramente a função de detectar conteúdo pouco explorado. Além disso, diferencia-se dos irmãos ao classificar os subtópicos em Gap Real, Oportunidade ou Saturado, e compara com o VidIQ, o que reforça sua identidade única.

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?

A descrição fornece contexto claro de uso (análise de lacunas de conteúdo em um nicho) e a analogia com o VidIQ ajuda a entender quando aplicar. No entanto, não menciona explicitamente quando não usar ou alternativas, como search_trending_topics, embora a finalidade distinta já indique isso.

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 burden. It discloses that the tool uses copywriting heuristics, assigns a 0–100 score, classifies into High/Medium/Low, and provides feedback, which conveys the approximate, read-only nature of the 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 two sentences, front-loaded with the main purpose, and each sentence adds value without redundancy.

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 no output schema and no annotations, so the description must convey the return format. It does: score, classification, and feedback. However, it omits potential limitations or edge-case behavior, but the overall purpose and output are sufficiently described given the schema richness.

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 descriptions for 'titles', 'niche', and 'regionCode'. The tool description does not add further detail about parameters, but it does mention the limit of 5 titles (matching maxItems). 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 uses a specific verb ('Estima') and clearly defines the resource ('CTR potencial de até 5 títulos candidatos'). It also details the scoring and classification output, distinguishing it from sibling tools like analyze_title_patterns or suggest_hook_angles.

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 used when an agent has 1-5 candidate titles to evaluate for CTR potential, but it does not explicitly state when to use it over alternatives or mention exclusions. The context is clear, but no alternatives are given.

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?

The description discloses the algorithm's inputs (views/day, channel size, video age) and notes it operates without proprietary data, adding useful behavioral context beyond basic purpose. Since no annotations are provided, the description carries the transparency burden; while it doesn't mention error handling or rate limits, these are less critical for a straightforward calculation tool.

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 long, with the primary purpose front-loaded in the first sentence. Every phrase earns its place, including the VidIQ comparison and the summary of methodology. There is no fluff or redundancy.

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 no output schema, and while the description indicates a numeric score, it does not describe the full response structure or how parameters like maxVideos and regionCode affect the score. The description explains the inputs to the calculation but lacks details on edge cases or interpretation. For a relatively simple tool with few parameters, this is adequate but not comprehensive.

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 provides descriptions for all three parameters, achieving 100% coverage. The description does not add any additional meaning to the parameters themselves, so it does not enhance what the schema already offers. Baseline score 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 calculates a keyword difficulty score (0-100) for YouTube ranking, using the verb 'calcula' and specifying the resource. It distinguishes itself from siblings by explicitly positioning as a replacement for VidIQ's Competition Score, a unique feature among the listed tools.

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 assessing keyword difficulty, mentioning it analyzes views/day, competitor channel size, and video age. It also provides context by stating it replaces VidIQ's proprietary tool, offering a clear use case. However, it does not explicitly contrast with sibling tools like get_keyword_stats or find_trending_keywords, so some inference is needed.

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 the key behavioral inputs (recent videos, views, channel size) and the output classification categories. It does not detail the return structure or edge cases, but for a read-only analysis tool it offers a reasonable level of 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 a single, efficient sentence with the action verb front-loaded. It conveys purpose, method, and output without unnecessary 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?

Despite having no output schema, the description implies the return value includes keywords with their classifications. With 3 documented parameters and clear analytical inputs, the description is complete enough for an agent to understand the tool's function, though it could be slightly richer on return format details.

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 100% as every parameter has a description. The tool description adds no specific parameter details beyond the schema, but the baseline is 3 when schema coverage is high, and the description does not conflict with or detract from the schema.

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 uses a specific verb 'Detecta' (detects) with a specific resource 'keywords emergentes no nicho' and explains the method (analyzing recent video patterns, views, channel size). It also mentions classification into Emergente, Crescendo, or Estável, which clearly differentiates it from sibling tools like get_keyword_stats or search_trending_topics.

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 finding emerging keywords but provides no explicit guidance on when to use it versus alternatives such as search_trending_topics or get_keyword_stats. There is no mention of exclusions or specific scenarios.

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 for behavioral disclosure. It explains that the tool identifies gaps and emerging keywords, suggests titles based on top video patterns, and organizes content by week with optimal days/times. While it does not mention limitations, data sources, or side effects, these are less critical for a non-destructive generation tool, and the description provides substantial behavioral context.

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 but somewhat long sentence that front-loads the primary action and efficiently enumerates key outputs. It avoids redundant wording, though the multiple clauses make it slightly dense, preventing a perfect score.

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 5 parameters, no output schema, and no annotations, the description must explain what the tool produces. It successfully communicates the calendar and its components (gaps, keywords, titles, schedule), but it does not detail the return format, such as whether the output is a list, table, or structured object. Given the tool's complexity, this is a noticeable but not critical 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?

All 5 parameters have descriptions in the schema, giving 100% coverage, so the baseline is 3. The description adds some context by specifying the 14-30 day range and weekly organization, which aligns with periodDays and postsPerWeek, but it does not offer deeper semantics beyond what the schema already provides.

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 'Gera um calendário de conteúdo de 14 a 30 dias para o nicho' (generates a content calendar), specifying the verb and resource. It also lists distinct sub-functions (identifying gaps, keywords, titles) that differentiate it from sibling tools like search_trending_topics or detect_content_gaps, showing it produces a consolidated calendar rather than a single analysis.

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 content planning by generating a calendar, but it does not explicitly state when to use it over alternative tools or provide exclusions. Sibling tools such as score_best_publish_window and analyze_title_patterns cover individual aspects, but the description does not direct users to choose this tool for a complete calendar, so the usage context is only implied.

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 burden of behavioral disclosure. It mentions 'authenticated channel' and 'returns', implying a read-only operation, but does not disclose potential rate limits, error handling, pagination, or side effects. This leaves significant behavioral ambiguity.

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 short sentences, front-loaded with the core purpose and a clear list of metrics. Every word contributes value, with no redundancy or irrelevant detail.

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?

Without an output schema, the description does not fully specify the return structure. It lists the metrics and mentions daily grouping, but omits details such as the exact response format, timezone handling, or authentication steps, leaving room for inference.

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 100%, so the baseline is 3. The description lists the available metrics which are already in the schema's enum, and adds the context of daily aggregation, but does not provide additional syntactic or default information beyond what the schema already offers.

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 identifies the tool as returning real channel analytics metrics via the YouTube Analytics API, listing specific metrics and stating daily grouping. This distinguishes it from sibling tools that focus on keywords, video metadata, or competitive analysis.

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 guidance is provided on when to use this tool versus alternatives. It doesn't mention prerequisites, exclusions, or comparisons with sibling tools such as list_channel_videos or benchmark_channel, leaving the agent to infer usage from the name and description alone.

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

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

With no annotations, the description carries the full burden. It discloses the required channel ID format and the fields returned, but it does not explicitly state that the operation is read-only or mention any side effects. The verb 'Lista' implies a safe read, but additional behavioral details are absent.

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 concise sentences: the first states the action, the second provides the use case, and the third states a requirement. No unnecessary detail or repetition.

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?

Without an output schema, the description usefully lists the returned fields (title, views, likes, duration, date). It also gives a use case. It could mention ordering or result limits, but those are covered in the input schema, so the description 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%, so the baseline is 3. The description restates the channel ID requirement ('começa com UC'), which is already in the schema, without adding new parameter-specific semantics.

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 'lists videos of a competitor channel' with specific fields (title, views, likes, duration, date). The word 'concorrente' distinguishes it from sibling tools that likely target the user's own channel, such as list_channel_videos.

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 says it's 'useful for analyzing competitor content strategy,' providing clear context for when to use it. However, it does not explicitly exclude alternatives or name sibling tools for comparison, so it lacks full when-not 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 carries full responsibility for behavioral disclosure. It goes beyond a simple 'analyzes keyword' by explaining how difficulty is estimated (channel size and video age) and that it suggests angles with less competition. This provides useful context about the tool's methodology, though it does not mention any limitations, rate limits, or exact response structure.

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, well-structured sentence that lists the main outputs clearly. It is concise, front-loaded with the core purpose, and contains no redundant information.

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 the absence of an output schema, the description does a reasonable job of explaining what the tool returns (top videos, difficulty estimate, angle suggestions). It does not detail the exact response structure or how to interpret the difficulty metric, but it provides enough context for the core use case.

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 fully covers all three parameters with descriptions, so the baseline of 3 applies. The description does not add any extra detail about maxVideos or regionCode beyond what the schema already provides, but it reinforces the keyword parameter implicitly through the phrase 'Analisa uma keyword'.

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 analyzes a keyword on YouTube and returns three specific outputs: top ranked videos, difficulty estimate based on channel size and video age, and low-competition angle suggestions. This is a specific verb+resource with a well-defined scope that distinguishes it from more focused sibling tools like estimate_keyword_difficulty or suggest_hook_angles.

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 a comprehensive keyword analysis, but it does not explicitly state when to use it instead of more specialized sibling tools, nor does it mention any exclusions or prerequisites. A clear use case is present, but alternative tool guidance is absent.

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 must fully disclose behavior. It only states that tags are extracted and that the video must be public. It does not mention output format, error behavior (e.g., video not found), rate limits, or whether authentication is required. This is a significant gap for a tool with no structured safety hints.

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, compact sentence that immediately states the action (extract), the target (tags from public YouTube videos), and the input method (video ID). It is front-loaded and contains zero 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?

For a simple one-parameter tool, the description is adequate: it states purpose, scope, and input. However, it does not explicitly describe the return value (e.g., a list of tag strings) or behaviors for edge cases like missing tags. Given the lack of an output schema, mentioning the return format would improve completeness. Still, the context is sufficient for basic usage.

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 provides 100% coverage for the videoId parameter, including format and an example. The description merely mentions 'pelo ID do vídeo' (by video ID), which reinforces the parameter but adds no new semantic information beyond what the schema offers. Baseline 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 uses a specific verb 'Extrai' (extracts) and clearly identifies the resource: tags from any public YouTube video by video ID. This distinguishes it from sibling tools that focus on trends, analytics, or content generation, making the tool's 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 provides a clear usage context: it works on any public YouTube video identified by its ID. It implies that private or unavailable videos are not supported, which is a useful constraint. However, it does not explicitly name alternatives or when-not-to-use cases, but given no sibling tool performs the same function, this is acceptable.

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 transparency burden. It discloses the channel selection behavior (using channelId for Brand Accounts, otherwise the authenticated channel) and lists the returned data fields. This adds meaningful context beyond the schema, though it doesn't mention pagination or rate limits.

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 the core purpose front-loaded, followed by important usage detail. Every sentence contributes essential information without redundancy.

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 3 optional parameters and no output schema, the description covers the essential aspects: what it returns, how to specify the channel, and the default behavior. It is complete enough for an agent to select and invoke the tool correctly without ambiguity.

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 100%, so the baseline is 3. The description adds value by providing a concrete example for channelId (UC_x33Gz4LvnOZuC_4v7Qucg for @dewtech) and explaining its purpose for Brand Accounts, which goes beyond the schema's basic 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 states the tool lists videos from a YouTube channel with specific fields (title, views, likes, publication date). It uses a specific verb ('Lista') and resource ('vídeos de um canal YouTube'), and implicitly distinguishes from siblings by focusing on video listing rather than analytics or keyword tools.

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?

Provides clear guidance on when to use channelId (for Brand Accounts) and explains the fallback to the authenticated channel when channelId is omitted. While it doesn't explicitly name alternative tools, the context makes its usage clear, and no exclusions are mentioned.

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 the full burden. It discloses that it uses the authenticated channel, analyzes views/day, groups by weekday and time range, and returns top 3 and bottom 2 slots. This provides meaningful algorithmic and behavioral insight beyond the tool name.

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 in Portuguese, front-loaded with the primary purpose and followed by grouping/output details. No redundant or vague wording.

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 one optional parameter and no output schema, the description covers the essential behavior: input (channel videos), analysis basis (views/day), grouping logic, and output (best/worst slots). It could mention edge cases or output format details, but it is sufficient for correct selection and invocation.

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 100%, with the single parameter videoCount already described (10-50, default 30). The description adds no additional parameter semantics, so baseline 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 a specific verb+resource: analyzes the authenticated channel's videos and returns the best day and time to publish. It distinguishes itself from sibling tools (e.g., keyword, tag, or metadata tools) by focusing on publish window optimization.

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 context is clear: this tool is used to determine optimal posting times based on recent video performance. It doesn't explicitly mention alternatives or exclusions, but the purpose is self-evident and separated from sibling tools.

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 full burden for behavioral disclosure. It indicates a read operation ('Busca' / searches) and mentions the output, but it does not disclose any side effects, rate limits, pagination, or safety characteristics. The description adds minimal behavioral context beyond the operation itself.

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, front-loaded sentence that directly states the tool's action and return value. There is no redundant information or filler, making it highly concise 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?

Given the tool's simplicity (4 simple parameters, no output schema), the description is fairly complete: it covers the core purpose and the return fields. The schema covers parameter details. However, it lacks any notes on result limits, ordering defaults, or potential variations in output, which would be useful for a complete picture.

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 schema already documents all parameters. The description only repeats the concept of 'tema ou keyword' for the query parameter, adding no extra meaning or nuance about parameters like order, maxResults, or regionCode.

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 with a specific verb ('Busca' / searches), resource ('vídeos em alta no YouTube' / trending YouTube videos), and scope (for a topic or keyword). It also lists the returned fields (title, channel, views, link), which distinguishes it from sibling tools like find_trending_keywords that search for keywords rather than videos.

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 context on when to use the tool: when searching for trending YouTube videos by topic or keyword. It does not explicitly mention alternative tools or when not to use it, but the purpose is unambiguous enough for an agent to select it appropriately among siblings.

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 adds context about using patterns from popular videos to prioritize effective hooks, but it does not disclose output format, limitations, or any side effects. The behavioral insight is limited to the methodology.

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 action and only contains essential information. It is concise with no redundant phrases.

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 generation tool with all parameters described in the schema, the description conveys the output (5 hook angles) and the method, which is sufficient for basic invocation. However, it lacks details about how regionCode or targetAudience affect results, though the schema provides some context.

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 100% with descriptions for all four parameters, so the baseline is 3. The description does not add additional meaning beyond what the schema already provides about how parameters influence the generated hooks.

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 generates 5 hook angles for video openings, using a specific verb and resource. It distinguishes itself from sibling tools like suggest_tags and analyze_title_patterns by focusing exclusively on hook angles for the video opening.

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 during video planning to select an effective hook, but it does not explicitly state when to use this tool versus alternatives such as suggest_tags. No exclusions or comparative guidance is provided.

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 burden. It discloses the internal process: analyzing top videos, extracting tag patterns, and grouping by relevance. However, it does not mention whether the tool is read-only, returns a list or structured data, or has any side effects. This is moderate transparency but leaves gaps.

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 primary action ('Sugere tags estratégicas'), and contains no filler. Every word adds value, efficiently conveying the analytical method and output grouping.

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 moderate complexity, three parameters well-described in the schema, but lacks an output schema. The description explains the general method but does not clearly define the return format (e.g., how tags are grouped, whether it's a list or scores). Given no annotations or output schema, the description should provide more explicit output details and distinctions from sibling tools, which it only partially does.

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 baseline is 3. The description does not directly explain parameters but references 'keyword' in the context of ranking videos. It adds minimal meaning beyond the schema—no additional detail about regionCode or videoCount—so it neither enhances nor degrades the schema's clarity.

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: 'Sugere tags estratégicas para um vídeo analisando os top vídeos que ranqueiam para uma keyword' (suggests strategic tags for a video by analyzing top ranking videos). This specific verb+resource form distinguishes it from sibling tools like get_video_tags, which simply retrieves tags rather than generating suggestions.

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 a use case (generating tag suggestions based on keyword ranking) but does not explicitly state when to use this tool versus alternatives such as get_video_tags or analyze_title_patterns. It lacks explicit when-to-use or when-not-to-use guidance, though the purpose is clear enough to infer typical usage.

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 adds useful context about the video being published and the channel being authenticated, but it does not disclose that existing metadata will be overwritten, whether changes are reversible, or any rate limits or permission requirements beyond authentication.

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 clear sentence in Portuguese, front-loaded with the action and resource. Every word is purposeful with no redundancy.

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 mutation tool with 5 parameters and no output schema, the description covers the essential what and when, and the schema covers the parameters. Missing details like return value or error behavior are not critical given the tool's simplicity, but could be added for full completeness.

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 100%, and the schema already describes each parameter including constraints (e.g., title max 100 chars). The description adds the word 'otimizados' (optimized), implying the values are pre-computed, but it does not add semantic detail to individual parameters beyond what the schema provides.

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 uses a specific verb 'Aplica' (applies) and clearly identifies the resource as 'título, descrição e tags' of a video. It also specifies scope ('já publicado no canal autenticado'), which differentiates it from the sibling tools that are all search/analysis tools.

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 context: it is for already-published videos on the authenticated channel, implying the tool is for modifying existing content rather than creating or analyzing. No explicit alternatives or exclusions are stated, but the sibling list contains no other update tools, making the use case clear.

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