x402vid AI Intelligence & Channel Growth for YouTube Videos

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

With no annotations, the description carries the burden and it provides useful behavioral context: it samples top comments rather than analyzing the full set, and it mines specific signal types. It does not mention authentication or cost behavior, but the x_payment parameter already documents that requirement, so this is not a major omission.

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 compact and front-loaded: a title line with the key insight followed by two short sentences. Every phrase ('questions', 'sentiment', 'content gaps', 'video ideas') adds value with 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?

An output schema is present, all parameters are documented, and the description covers the sampling approach and output themes. The main missing piece is explicit guidance on when to prefer this tool over sibling analysis tools, but that is a minor gap given the clarity of the comment-specific scope.

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 parameter descriptions already carry the semantic load. The tool description's 'Samples top comments' aligns with max_comments but adds little meaning beyond the schema's own 'Comments to sample' 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 names the exact resource ('YouTube comment intelligence') and specific actions ('Samples top comments and mines audience questions, sentiment, and ... video ideas'). This is clearly distinct from sibling tools that focus on topics, channel comparisons, or video-level intelligence.

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: when you need audience questions, sentiment, or content gaps from comments. However, it does not explicitly state when to choose this over siblings like analyze_youtube_topic or get_video_intelligence, nor does it provide exclusions or alternatives.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotenceHint=true, so the safety profile is covered. The description adds useful behavioral context by explaining the internal workflow (searching, fetching transcripts, running analysis) and the nature of the returned report, going beyond what annotations provide. It does not mention the required x_payment, but that is documented in the schema.

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 compact and well-structured: the first sentence gives the core purpose, and the following sentences elaborate on the process and deliverables. Every sentence carries meaningful information, and there is no redundant or filler content.

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

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

The description explains the output format in enough detail to compensate for the lack of an output schema, and it covers the overall process from search to analysis. It lacks explicit comparison to sibling tools and does not mention payment/cost, but the schema covers the x_payment parameter. Overall, it is sufficiently complete for a read-only research 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 input schema provides complete descriptions for all three parameters, including defaults, max, and the required x_payment payload. Schema coverage is 100%, so the description does not need to add param-level detail. The description adds only marginal context about videos being top-ranked, which is not directly parameter-specific.

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 what the tool does: produces a multi-video AI research report on a topic. It details the pipeline (search, transcripts, cross-video analysis via Muse Spark 1.3) and explicitly names the report contents, which distinguishes it from sibling tools focused on single-video intelligence or channel growth.

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 to use the tool (when you want a cross-video research report on a topic), but it does not explicitly state when to prefer alternatives like search_and_rank_videos or get_video_intelligence. No when-not-to-use conditions or exclusions are provided, leaving the guidance at an implied level.

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 a key latency behavior ('Slow call — allow up to 5 minutes') but does not state whether the operation is read-only, what side effects exist, or how results are delivered.

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 tightly written sentences front-load the core purpose, then add output specifics and the critical latency warning. No filler 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?

For a tool with an output schema, the description covers what it does, what the comparison yields (scoreboard, packaging graades, tactics, gaps), and the main operational caveat (5-minute runtime). The payment requirement is already in the schema, so not restating it is acceptable.

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% — every parameter (channels, topic, x_payment) is already documented. The tool description does not add parameter semantics beyond what the schema states, so the baseline holds.

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 opens with a specific verb+resource ('Multi-channel YouTube competitor comparison') and clarifies scope ('Compare 2-3 channels head to head'). It is distinct from siblings focused on topics, videos, or single-channel growth, so an agent can tell what it does without opening the schema.

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?

It provides clear context: the tool is for head-to-head channel comparisons, and the 'Slow call — allow up to 5 minutes' caveat sets an important expectation. However, it does not explicitly name alternatives or say when not to use it.

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 behavioral disclosure burden. It transparently explains the two-step process — fetching captions and running them through Muse Spark 1.3 — and lists the output sections. It does not disclose failure modes or the payment requirement, but the latter is clearly marked in the x_payment parameter description.

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 compact and front-loaded: the first sentence states the core action, the second explains the process and outputs. There is no filler or redundant restatement of the title.

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

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

The description covers the main behavior and outputs, and an output schema exists so return-value details are not required. However, it omits usage guidance relative to the sibling tools and does not mention the required x_payment prerequisite outside the schema, leaving a small completeness gap for an agent selecting among multiple video-related tools.

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 all three parameters carrying descriptions. The tool description itself adds no parameter-level detail beyond naming video_url and the output process. Baseline 3 is appropriate since the schema already documents each parameter adequately.

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 opens with a specific verb-plus-resource statement: 'Extract and AI-analyze a YouTube video transcript.' It then enumerates the structured report contents, making it clear what the tool produces and distinguishing it from sibling tools like search_and_rank_videos or video_preview.

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 you need an AI-generated intelligence report from a YouTube video's transcript. However, it does not explicitly state when to prefer this tool over siblings like analyze_youtube_topic or grow_channel_report, nor does it mention any exclusions or alternative conditions.

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 what the tool processes (recent uploads, packaging, titles), what it evaluates (niche demand vs. top-performing videos), and its output (90-day action plan). It also explicitly warns about slow execution time. It does not explicitly mention payment or read-only behavior, but these are at least partially covered by schema descriptions and the audit nature of the 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 compact and well-organized: purpose first, then input expectations, process details, and a latency warning. Every sentence contributes new information with no fluff 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?

Given the output schema is provided and all parameters are documented, the description completes the picture by stating the tool's purpose, input types, key processing steps, output type, and the important 'up to 5 minutes' latency caveat. An agent has enough context to select and invoke this tool correctly without further assumptions.

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 all three parameters are documented in the schema. The description repeats guidance for channel_input ('Give a channel URL, @handle, or channel ID') but adds no additional parameter-level meaning beyond the schema. This matches the baseline score for well-covered schema descriptions.

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 and resource: 'YouTube channel growth audit' and enumerates concrete outputs (packaging grades, rewritten titles, niche demand validation, 90-day action plan). It is distinct from sibling tools like analyze_youtube_topic or search_and_rank_videos because it targets a full channel-level growth audit rather than a narrower analysis or search.

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 communicates when to use the tool: when a user wants a channel growth audit with actionable recommendations. The 'Slow call... allow up to 5 minutes' warning provides an important timing consideration. However, it does not explicitly name alternatives or state conditions when another sibling tool would be a better fit.

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 and addresses a common concern by stating the check is free ('no payment'). 'Health check' plus 'verify the server is live' conveys a non-mutating status probe, and since there are no inputs, no permission or parameter side-effects need disclosure.

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 deliver the key facts—free and purpose—with no fluff. The purpose is front-loaded and every word earns its place.

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 no-input health check with an output schema, the description is complete: it states that it is free and specifies the verification purpose. The output schema covers return details, so nothing essential is missing.

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 the baseline is 4; there is no parameter meaning for the description to add. The input schema already fully defines the empty input contract.

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 ('verify') and resource ('server is live') tied to a health check, and the service is clearly distinct from the analytics-focused sibling tools. Even without reading schemas, an agent can tell what this tool does.

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 'Use to verify the server is live' provides a clear, actionable when-to-use condition. It does not mention exclusions or alternatives, but no sibling tool overlaps with this health-check purpose, so this is a minor gap.

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 behavioral disclosure burden. It does disclose the research and AI-scoring behavior and lists the output dimensions. However, it does not mention the required x_payment/auth aspect, external data fetching, or any rate limits or side effects, leaving moderate 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 concise and front-loaded with the core purpose, followed by a clear list of scoring dimensions. It avoids excess words, though 'YouTube niche and keyword research' and 'Top videos by view count for a keyword' are slightly redundant.

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 presence of an output schema and a fully documented input schema covers return values and parameter meanings. However, the description omits usage context relative to its many siblings and does not flag the payment/payload requirement, making it incomplete for an agent deciding whether to call this 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 each parameter (keyword, x_payment, num_videos) is already documented in the schema. The description adds little to parameter semantics beyond the schema, but the baseline of 3 applies because the schema handles the explanatory burden.

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

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

The description states a specific action ('YouTube niche and keyword research') and details the output: top videos by view count, scored for demand, saturation, opportunity grade, underserved angles, and title patterns. It is clear and specific, but it does not explicitly distinguish itself from sibling tools like analyze_youtube_topic or search_and_rank_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?

There is no guidance on when to use this tool versus alternatives such as analyze_youtube_topic or compare_channels. The context is only implicit ('niche and keyword research'), with no conditions, exclusions, or typical scenarios, so an agent must infer when this tool is appropriate.

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 nature of the output: a niche CPM band, estimated monthly AdSense range based on channel stats, and an AI-generated rationale with advice. This goes beyond a simple purpose statement by clarifying that results are estimates and include interpretative commentary. With no annotations present, this provides useful behavioral context, though it does not mention payment requirements or failure behavior.

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 compact and front-loaded: the first line states the core purpose, and the second elaborates on output components. Every sentence contributes information without padding or repetition. This is appropriately sized for a straightforward estimation 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?

Given the output schema exists and the input schema has full coverage, the description is sufficient for an agent to understand what the tool produces and roughly how it derives the estimate. It could be more complete by explicitly noting the payment requirement and the optional nature of monthly_views, but those are already present in the schema. Overall, nothing essential is missing for correct 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 description coverage is 100%, so the schema already documents all three parameters adequately. The tool description adds little parameter-level meaning beyond referencing channel stats as the basis for estimates. This matches the baseline expectation when the schema does the heavy lifting.

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's domain and resource: YouTube revenue estimation with CPM benchmarks and ad earnings ranges. It is distinguishable from siblings because no other sibling tool mentions revenue or AdSense. It lacks an explicit imperative verb, but the noun-phrase style is still 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?

Usage is implied rather than stated: an agent can infer this tool is for estimating channel revenue, especially from CPM benchmarks and AdSense ranges. However, the description gives no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. Among the siblings, none directly overlaps, so the lack of routing is a minor gap.

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 burden, and it does well by disclosing the two-stage behavior: YouTube Data API search followed by Muse Spark AI ranking by relevance, credibility, and content quality. It also sets expectations for output with 'curated shortlist with reasoning.' It does not mention the x_payment prerequisite, though the schema already marks it required.

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 with no fluff. The core action is front-loaded in the first sentence, and the second sentence adds just enough mechanism detail without restating the schema.

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?

An output schema exists, so return values do not need explanation. The description covers purpose, methodology, ranking criteria, and output style. The only missing piece is explicit mention of the x_payment requirement, but that is clearly documented in the schema, so the tool is adequately specified for 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?

The input schema has 100% coverage and describes search_term, num_videos, and x_payment. The description adds no parameter-level details beyond the schema, but the mention of relevance ranking indirectly reinforces that search_term is the core query. Baseline 3 is appropriate since the schema handles parameter 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 opens with a specific verb and resource: 'Search YouTube and return AI-ranked results with relevance reasoning.' It clearly states the tool's function, the ranking mechanism, and the output format, and it is distinct from siblings like video_preview or get_video_intelligence because it emphasizes AI ranking and a curated shortlist.

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 gives clear context: use this tool when you need to search YouTube and get AI-ranked, relevance-reasoned results. It does not explicitly name alternatives or exclusion conditions, but the 'AI-ranked' and 'curated shortlist' wording helps disambiguate from raw search or channel-analysis 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?

With no annotations, the description carries the behavioral burden. It does convey that this is an analysis/read-style tool (sampling top Shorts and analyzing them), which implies no side effects. It does not explicitly describe the payment requirement or operational caveats, though the payment field is documented in the schema.

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 short and front-loaded with the main topic, and the second sentence gives concrete output themes. It loses a point for redundancy: 'hooks, pacing' appears twice, and 'breakout patterns' overlaps with 'repeatable formats'.

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 moderate complexity, the description plus fully documented parameters and an output schema is mostly sufficient. It explains the input (topic), the sampling approach, and the key analytical dimensions. The main missing piece is explicit alternative routing, but that is more of a usage-guideline 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 100%, so the schema already explains search_term, num_videos, and x_payment. The description adds thematic context ('new creator can copy') but no new parameter-level meaning, so the 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 identifies the resource (Top YouTube Shorts for a topic) and the analytical focus (hooks, pacing, breakout patterns, repeatable formats), so an agent can infer what this tool does. It distinguishes itself from siblings by narrowing to Shorts-level, creator-copyable patterns, but it does not explicitly name or contrast a sibling tool.

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: use it to research a topic's top Shorts and extract creative patterns. However, there is no explicit guidance on when to choose this over analyze_youtube_topic, get_video_intelligence, or niche_research, and no exclusions are stated.

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 behavioral burden. It adds useful context: generation is fast and purely AI-driven, and the output is a shoot-ready script. However, it does not mention the required x_payment/cost side effect or any rate/auth considerations, leaving part of the behavioral profile to the schema.

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 function, then the workflow relationship. No filler; every phrase either states what the tool produces or how it fits with shorts_intelligence.

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 a complete input schema and an output schema present, the description is largely sufficient: it identifies the input (topic), the output components (hook, beats, CTA), and the upstream tool. It loses one point for cryptic workflow language and not explicitly flagging the payment requirement in prose, though the schema documents it.

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 add parameter-level detail beyond 'from a topic'; duration_secs, hook_style, and x_payment are left entirely to 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 states a specific function: producing a Shorts script with hook, beats, and CTA from a topic. It also distinguishes itself from the analysis siblings by explicitly positioning against shorts_intelligence ('winning patterns in, shoot-ready script out').

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 conveys clear workflow context: it closes the loop with shorts_intelligence, implying it should be used after that analysis tool to turn winning patterns into a script. It does not explicitly list when-not-to-use or alternative options, but the name and prose make the primary use case obvious.

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 it well: it says the tool is fast and pure AI, specifies that it produces three rewrites under 60 chars, and clarifies that no video lookup is performed. It does not mention payment/cost behavior, but that is represented in the input schema.

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 uses two short sentences to deliver the core purpose, speed, output count, length constraint, keyword placement, tags, and the no-lookup boundary. Every sentence earns its place and the main function is front-loaded.

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 low-complexity generator with three parameters and an output schema, the description is nearly complete: it states what the tool produces and what it does not need. The only notable omission is an explicit statement that a payment payload is required, which the schema mentions but with an inconsistent required/optional marker.

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 applies; the tool description itself adds no parameter-level detail. The schema adequately describes 'title' as the working title, 'niche' as optional keyword context, and 'x_payment' as a Base64 payment payload, though the x_payment text says 'required' while the schema structure marks it optional.

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 is a YouTube title and description optimizer and enumerates its exact outputs: three CTR-ranked rewrites under 60 chars, a keyword-front-loaded description draft, and tags. The 'No video lookup needed' note distinguishes it from video-analysis siblings.

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 purpose makes the primary use case obvious, and 'No video lookup needed' gives a concrete boundary: it can be called directly with a working title instead of first fetching video data. It does not explicitly name alternative tools or list when-not-to-use scenarios, but none of the siblings clearly compete with title optimization.

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 of behavioral disclosure. It usefully states that the tool performs no AI analysis and returns timestamped, linkable Markdown with a video metadata header, which goes beyond the schema. It does not explicitly mention payment or auth side effects, though the x_payment parameter description covers that requirement.

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

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

The description is concise and front-loaded with the core purpose, followed by the output format and a clear routing instruction. Every sentence earns its place and there is no redundant 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?

Given the simple two-parameter schema, the presence of an output schema, and explicit sibling differentiation, the description is largely complete. It could be slightly more explicit about the required x_payment and the read-only nature, but those are partially covered by the schema 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 description coverage is 100%, so the schema already documents both video_url and x_payment. The tool description does not add additional parameter-level meaning, which is acceptable given the high schema coverage.

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 a raw YouTube transcript as a Markdown file and explicitly notes 'no AI analysis.' It also names the differentiator by pointing to get_video_intelligence for AI summaries, making its purpose distinct from siblings.

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 explicitly says 'For AI summaries use get_video_intelligence,' giving the agent a clear when-not-to-use instruction. The phrase 'raw YouTube transcript' plus the contrast with AI analysis conveys the appropriate use case effectively.

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 behavioral burden and does it well: it discloses the return shape ('Raw captions with timestamps, nothing else') and positions the call as a cheap, non-AI read operation. It stops short of noting failure modes (e.g., videos without captions) or that x_payment is mandatory, though the schema covers the latter.

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

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

Three sentences with zero waste: the core purpose is front-loaded, the exact output is stated next, and sibling routing closes it out. The slight overlap between 'plain text' and 'raw captions' reinforces rather than bloats the message.

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 (2 parameters) and has an output schema, so the description need not explain return values. Purpose, output nature, and alternatives are all covered; the only gap is that the description does not reinforce the payment requirement that the schema flags as required.

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. Both video_url and x_payment are already documented in the schema, and the description adds no parameter-level meaning 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 and resource: 'Bare YouTube transcript extraction as plain text.' It sharpens scope with 'Raw captions with timestamps, nothing else.' and explicitly distinguishes itself from the siblings transcript_markdown and get_video_intelligence, so an agent can select it correctly without inspecting schemas.

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?

'For a formatted file use transcript_markdown; for AI summaries use get_video_intelligence' explicitly names alternatives and the conditions that select them. 'Cheapest, no AI' also frames when this tool is the right choice, so no inference is required.

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 behavioral disclosure, but it only summarizes the result and niche scoring. It omits the mandatory x_payment requirement, which appears only in the schema parameter description, and gives no hint about auth, failure modes, 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 short and front-loaded with the core behavior in the first sentence. The second sentence adds useful context about the optional niche mode, though 'ways to ride trends this week' is slightly promotional and vague.

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 presence of an output schema and 100% parameter coverage reduces the burden on the prose, but the description fails to surface the required x_payment usage, which is a critical invocation condition. It also leaves sibling differentiation implicit, so an agent must inspect schema or rely on the tool name to choose 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 coverage is 100%, so the baseline is 3. The tool description adds real semantic value for the niche parameter by explaining that it produces AI picks and trend-riding suggestions, which is more than the schema's 'score trends against' phrasing. Other parameters remain adequately documented by 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 identifies the resource as YouTube trending by region and states the output is today's most-popular videos with views and channels. It adds a differentiator—niche scoring and AI trend picks—but does not explicitly distinguish itself from siblings like search_and_rank_videos or niche_research.

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 'optionally scored against a niche' and 'pass a niche to get AI picks' implies when the niche mode is relevant, and 'by region' implies regional filtering. However, there is no explicit when-to-use or when-not-to-use guidance relative to sibling tools such as search_and_rank_videos or compare_channels.

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?

Discloses that the transcript is capped at ~2000 chars and that no AI analysis is included. Though no annotations are provided, the description covers the key behavioral boundary (free vs paid, truncated output). It doesn't mention error cases or authentication requirements, which with no annotations leaves some 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?

Compact and front-loaded with the core value proposition first. Each sentence earns its place: what it returns, when to use it, and its limitations. No redundancy or waste.

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?

Complete for a simple single-parameter preview tool with an output schema covering the return values. The absence of annotations is partially mitigated by the description's clear scoping. Could add what happens on invalid URLs, but the output schema covers the return format.

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 schema already documents the video_url parameter. The description doesn't add extra detail about URL formats beyond the schema's 'YouTube video URL or video ID' explanation. Baseline 3 is appropriate since the schema does the heavy lifting.

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

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

The description clearly states the tool provides a free YouTube video preview with title, channel, stats, and transcript excerpt. It distinguishes itself from the paid get_video_intelligence tool by explicitly positioning itself as a free taster.

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?

Explicitly positions this as a taster for get_video_intelligence and states the payment distinction. An agent can clearly understand when to use this free preview vs the alternative paid intelligence tool.

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