x402vid AI Intelligence & Channel Growth for YouTube Videos

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

The annotations already cover read-only, open-world, non-idempotent, and non-destructive behavior. The description adds useful behavioral context beyond annotations: it samples top comments rather than exhaustively retrieving all comments, and it mines specific insight types. It does not describe payment requirements, though the schema does.

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 two sentences are short and front-loaded, but the second sentence largely repeats the first: 'audience questions, sentiment, and the video ideas viewers are begging for' restates 'questions, sentiment, content gaps.' Only the sampling detail in the second sentence adds new 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 100% schema coverage, an output schema, and annotations covering safety, the description provides enough to understand the tool's core function and the fact that comments are sampled. It could be stronger on when to choose it over sibling analysis tools, but nothing essential for invocation 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?

Schema description coverage is 100%, so the schema already documents video_url, x_payment, and max_comments. The description's mention of sampling top comments loosely connects to max_comments, but it adds no syntax, format, or boundary details beyond 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 verb and resource: fetching and mining a YouTube video's comments. It clearly names the outputs (questions, sentiment, content gaps), making the tool's role distinct, but it does not explicitly differentiate itself from related siblings such as get_video_intelligence or analyze_youtube_topic.

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

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

There is no explicit when-to-use guidance, no when-not-to-use conditions, and no alternatives named. The agent can infer that it is for comment analysis, but the description does not route between this tool and similar 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?

Annotations already declare readOnly=true, destructive=false, openWorld=true, so the safety profile is covered; the description usefully adds that the tool downloads transcripts and runs an external model (Muse Spark 1.3), a multi-step, non-instant behavior. It omits the x402 payment/credits requirement implied by the x_payment parameter, which is a meaningful calling cost the agent should know about.

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

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

Two short sentences, front-loaded with the core action and outcome, with no padding. There is mild redundancy between 'Fetch top videos... with a multi-video AI research report' and 'Searches for the top videos on a topic', but it stays tight.

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 an output schema present, the description need not enumerate return fields (it does anyway), so return documentation is a bonus rather than a gap. The missing piece is the payment/authentication reality of the x402 flow, which the agent needs to successfully invoke 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% (topic, video count with default/max, payment payload), so the schema already carries parameter meaning. The description adds nothing beyond it, which meets the baseline 3 for a fully documented 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 verb pipeline (search, download transcripts, cross-video analysis) and a concrete resource (top videos on a YouTube topic), producing a named artifact (research-grade report). It does not, however, contrast itself against the several overlapping siblings such as search_and_rank_videos, get_video_intelligence, batch_transcripts, 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 'multi-video research report' implies the use case, but there is no explicit when-to-use, when-not-to-use, or named alternative. An agent facing 21 siblings with adjacent capabilities gets no routing signal from this text.

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 readOnly/idempotent/non-destructive/openWorld, so the safety profile is covered. The description adds genuinely non-structured context: no AI/LLM cost, a 20-video cap, and per-video status isolation with bundled metadata, which tells the agent a partial failure won't abort the whole batch.

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?

Front-loaded with the core action and scope, and the cap is stated up front. Slight redundancy: 'free, no AI' and 'Free, no payment required' restate the same point in two places.

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-value detail is unnecessary. The description covers input format, batching limit, and failure isolation. The only gap is explicit routing against the single-transcript siblings.

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

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

Schema coverage is 100%, so the baseline is 3, but the description adds cross-parameter meaning: the two inputs can be combined ('and/or') and the batch is capped at 20. That interaction is not spelled out by the schema alone.

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

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

States a specific verb and resource ('Fetch transcripts + metadata'), plus the distinguishing scope ('for many YouTube videos at once'). This separates it cleanly from siblings transcript_text and transcript_markdown, which imply single-video retrieval.

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?

Gives input-shape guidance ('Comma-separated video URLs/IDs and/or a playlist URL') and a hard cap, which implies when it is appropriate, but never explicitly names the single-video alternatives or states when to prefer them. Usage is inferable rather than 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?

Annotations already cover read-only and non-destructive behavior, and the description adds genuinely useful context beyond them by warning that this is a slow call and may take up to 5 minutes. It also clarifies the generative scope of the outputs, though payment/auth is left to the parameter schema rather than the description prose.

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 one-line summary of the kit, a target-user prompt, and a latency warning. Every sentence earns its place, with no filler or repetition of annotation fields.

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 rich input schema and presence of an output schema, the description covers the key invocation context: new channel, topic, expected deliverables, and slow runtime. The only notable gap is that the payment payload is described as required in the parameter schema but not mentioned in the description prose and is not in the schema's required list, which could confuse a caller.

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 of 3 applies. The description mostly repeats the idea of providing a topic and does not add meaning for tone, audience, duration_mins, or x_payment beyond what the input schema already states. The parameter descriptions are already explicit, so no penalty is needed, but the tool description adds no extra parameter value.

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

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

The description states a clear action and resource: validate a niche, script video #1, and plan videos 2-5, and lists concrete deliverables such as a launch script, 5-video cluster, teaser, and thumbnail spec. It is not a tautology, but it does not explicitly contrast with sibling tools like niche_research or thumbnail_concept, so it misses the top tier.

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 'New to YouTube? Start here' gives an explicit audience and entry-point condition, and 'Give a topic; get...' indicates the minimal invocation pattern. However, it does not state when not to use the tool or name alternatives, so the guidance is clear but not fully exclusionary.

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 idempotentHint=false enjoy the safety profile. The description adds useful behavioral context by stating that threads are selected by reply count and that AI identifies debates, consensus splits, unanswered questions, and replies worth pinning. It does not explain what idempotentHint=false means operationally or that the x_payment requirement is part of the call, though the schema does.

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. The second sentence provides meaningful detail about the AI verdicts. The closing phrase 'Closes the StableSocial replies gap' is marketing-flavored and low-information, but it is short and does not substantially harm readability.

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 annotations cover safety, the description conveys the core operation and selection heuristic well enough. However, it is missing guidance on when this tool is preferable to analyze_comments or get_video_intelligence, and the payment requirement could be surfaced in the description rather than only in the schema. It is adequate but not 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 tool description itself adds no parameter-level meaning; it does not mention x_payment, max_threads, or max_replies_each. The schema already documents the parameters, including the x_payment requirement and max/default values, so the description neither helps nor hurts beyond the 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 starts with a specific verb and resource: 'Mine reply threads under top YouTube comments with AI verdicts.' It clearly identifies what the tool operates on and what it produces. However, it does not explicitly differentiate it from sibling tools like analyze_comments or get_video_intelligence, so it earns a 4 rather than a 5.

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 direct guidance on when to use this tool versus alternatives. The phrase 'Closes the StableSocial replies gap' vaguely implies a niche use case but is jargon and not actionable. No sibling tools are named, and no exclusions or preconditions are 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?

Annotations already declare readOnlyHint, idempotentHint=false and openWorldHint, so the safety profile is covered. The description adds meaningful context annotations do not carry: it is a slow call needing up to 5 minutes, which materially affects agent invocation strategy.

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?

Short and front-loaded, with the latency warning placed last where it is easy to catch. The phrase 'compare head to head' is repeated across both sentences, a minor redundancy that keeps it from a 5.

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 need no explanation, and the schema covers the payment requirement. The description supplies the latency expectation and output shape, leaving only the explicit paywall/payment-requirement note absent from the prose.

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 topic, channels, and the required x_payment payload. The description only reinforces the accepted channel count and adds no format or syntax detail beyond the schema, making the baseline 3 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?

States a specific verb (compare) and resource (2-3 YouTube channels) plus the deliverable it returns (scoreboard, packaging grades, tactics, gaps). It is clearly distinct from single-channel siblings like analyze_youtube_topic, but it never names a sibling to route against, so it stops short of a 5.

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 '2-3 channels' bound implies when the tool applies, but there is no explicit when-to-use versus alternatives (e.g. grow_channel_report or niche_research) and no stated prerequisites beyond the payment parameter living in the schema.

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 cover readOnly, non-destructive, and open-world hints. The description adds valuable context beyond that: it reveals the underlying pipeline (caption data download + Muse Spark 1.3 model) and the report structure, which helps set expectations. It stops short of disclosing rate limits, latency, or failure modes.

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 tight sentences: the first front-loads the core action, the second details the pipeline and output. No fluff, though the second sentence's list is slightly crammed.

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 an output schema exists, the description need not explain return values — and it doesn't, but it does summarize the report contents, which is a nice touch. For a read-only, idempotent-annotated tool, the description is nearly complete, only missing guidance on the required x_payment parameter's role in the calling flow.

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 documents video_url (preferred), video_url_or_title (alias), and x_payment (required). The description adds no parameter-level detail, so the baseline 3 applies 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?

States a specific verb (fetch transcript + AI-analyze) and resource (YouTube video), and enumerates the exact output sections. It clearly distinguishes itself from siblings like transcript_text and transcript_markdown by emphasizing the AI-generated intelligence report rather than raw transcript delivery.

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 (when you want an analyzed report vs a raw transcript) but never explicitly states when to choose this over transcript_text, transcript_markdown, or analyze_youtube_topic. No exclusions or prerequisites 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?

Annotations cover the safety profile (readOnly, non-destructive, open-world), and the description adds two genuinely useful details not in structured data: the call is slow (up to 5 minutes, consistent with idempotentHint=false) and it audits multiple videos. The payment requirement is mentioned only via the schema's x_payment field, so cost/permission expectations remain partly implicit.

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?

Front-loads the core action, then enumerates the audit outputs and the latency caveat. Roughly four short lines with no filler; only the trailing latency note is slightly detached from the flow.

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 need not be explained, and the description still covers input forms, audit scope, and the slow-call expectation. An agent has everything needed to invoke it correctly, including the multi-minute timeout allowance.

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 format, x_payment, and channel_input are all documented in the schema. The description's 'give a channel URL, @handle, or channel ID' essentially restates the channel_input schema description without adding syntax or edge-case detail, so baseline 3 applies.

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

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

States a specific verb and resource — fetch a channel's stats and audit its growth — plus the concrete outputs (packaging grades, rewritten titles, niche validation, 90-day action plan). It is clearly distinguishable from most siblings by content, but never names a sibling tool (e.g. channel_launch_kit, compare_channels) to disambiguate the overlapping 'channel growth' space.

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?

Gives the accepted input forms (URL, @handle, channel ID) and implies the use case is a growth audit, but offers no explicit when-to-use/when-not-to-use guidance or routing against alternatives like channel_launch_kit. Usage must be inferred from the purpose statement.

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 provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=true, which cover the safety and idempotence profile. The description adds the 'no payment' aspect, which is useful context beyond annotations, but doesn't describe response format or potential latency. It adds minor value without contradicting annotations.

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 waste. It front-loads the key fact (free health check) and states the primary use case (verify server is live). 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 tool with no parameters, an output schema that covers the return format (not provided in the context signals but stated), and annotations that define safety (readOnly, idempotent, non-destructive), the description is complete. The agent has all needed information to invoke it correctly.

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

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

The tool has 0 parameters)Skip content

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 does a free service health check with no payment required, and its purpose is to verify the server is live. It uses a specific verb ('verify') and resource ('server'), distinguishing it from sibling tools that analyze content or generate reports.

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 checking server liveness, and notes it's free with no payment. However, it doesn't explicitly state when to use this tool vs alternatives, nor does it mention any exclusions or conditions that would make this tool inappropriate. The context is clear but limited.

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, openWorldHint, and non-destructive/non-idempotent behavior, so the bar is lower. The description still adds real context beyond them: keyless and AI-free execution, multi-seed expansion across modifiers, and frequency-ranked output grouped into intent clusters, which tells the agent what the call actually produces.

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 action and the sibling positioning leading. The parenthetical '(better than $0.06 rivals)' is competitive marketing noise that does not help an agent decide or invoke, so it is not quite pristine.

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 an output schema present, return values need not be explained, and the description covers pipeline position, expansion behavior, and output grouping. It omits any mention that a payment payload is required to call it, but that gap is filled by the schema's x_payment description.

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 lang, region, x_payment, and seed_topic are already documented. The description only reinforces seed-topic behavior (comma-separated multi-seed, modifier expansion) and adds no syntactic detail beyond the schema, making 3 the correct 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?

States a specific verb+resource (scrape YouTube autocomplete suggestions for a seed topic) and positions itself relative to a sibling by calling itself 'upstream of niche_research'. An agent can distinguish this from trending_on_youtube or niche_research without opening a 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?

The phrase 'upstream of niche_research' implies a pipeline position, which gives some when-to-use signal, but there is no explicit statement of when to prefer this over siblings like trending_on_youtube or search_and_rank_videos, and no exclusions. Usage is inferred rather than 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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=false, and destructiveHint=false, so the safety profile is covered. The description adds that the tool surfaces concurrent viewers and an AI brief, but says nothing about the payment/auth requirement implied by x_payment, rate limits, or freshness of the live data, leaving meaningful behavioral context on the table.

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 paragraphs, front-loaded with what is fetched and followed by a concrete value proposition. The promotional tone ('before humans finish watching') and the unexplained '(trader sentinel)' aside are minor padding but the text is efficiently sized.

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 an output schema present, the description need not explain return values, and annotations carry safety semantics. The notable gap is that a paid x402 endpoint is never mentioned in the description even though x_payment is effectively needed to call it, and there is no indication of result freshness or region behavior.

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 all five parameters (topic, region, x_payment, num_videos, include_upcoming) are already documented in the schema. The description adds no parameter-level syntax or format detail beyond what the schema provides, so the baseline of 3 is appropriate.

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

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

The description names a specific verb (fetch) and resource (live-now and upcoming YouTube streams), plus the added value of a 3-sentence AI brief and concurrent-viewer data. The 'live-now/upcoming' scope implicitly differentiates it from siblings like trending_on_youtube or search_and_rank_videos, but no sibling is explicitly referenced, so it falls just short of a 5.

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 line 'Cron it to flag launches and sentiment shifts' implies a recurring monitoring/monitoring-for-change use case, which is helpful context. However, it gives no explicit when-not guidance and names no alternative tool to use for related needs (e.g. trending_on_youtube for general trending), so usage 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint=false and destructiveHint=false, so the safety profile is covered. The description adds that results are AI-scored and what scoring dimensions exist, but omits any mention that the call is paid (x_payment is required) and that sampling is capped at 12 videos — meaningful behavioral context for invocation.

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, front-loaded sentences with no filler; the core action leads and the scoring dimensions follow. The second line is a fragment but reads cleanly and earns its place by naming the outputs.

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 need no explanation, and annotations cover the safety profile. The definition is nearly complete for a read-only research tool; the only real omission is failing to surface the paid-transaction prerequisite outside the schema.

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 keyword, x_payment, and num_videos are all documented in the schema itself. The description adds no syntax, format, or constraint details beyond that (e.g., it does not restate the max-12 cap or the payment payload format), so baseline 3 applies.

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

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

States a concrete verb+resource ('scrape top YouTube videos by views for any keyword') and enumerates the AI scoring dimensions, so the agent knows exactly what the tool produces. It does not, however, distinguish itself from close siblings like search_and_rank_videos or analyze_youtube_topic, which occupy similar territory.

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 keyword-research framing and the opportunity/saturation output imply when this tool is useful (picking a niche), but there is no explicit when-to-use, when-not-to-use, or named alternative. Usage must be inferred from the output list rather than 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?

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false, so the safety profile is covered. The description adds that it pulls live channel stats and produces an AI-generated rationale, which hints at external calls, but it omits the x402 payment requirement — a notable behavioral precondition for a paid 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?

Front-loaded with the core action, then the output list. Concise and readable, though the second sentence is a bit listy and could be tightened.

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?

Output schema exists and annotations cover the safety profile, so the description needn't explain return values. However, for a paid, open-world, non-idempotent estimation tool, the absence of any note about the x402 payment flow or estimation fallback logic leaves a meaningful gap.

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

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

Schema description coverage is 100%, so the schema already documents x_payment, channel_input, and monthly_views. The description adds no parameter-level detail; the baseline of 3 applies 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?

Clear verb+resource: 'Fetch channel stats and estimate ad revenue.' It also enumerates the outputs (CPM band, monthly AdSense range, AI read). It does not explicitly distinguish itself from siblings like niche_research or compare_channels, keeping it at 4 rather than 5.

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 statement of when to use this tool versus the many revenue-adjacent siblings (niche_research, compare_channels, grow_channel_report). The agent must infer context from the name 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint=false and destructiveHint=false, covering the safety profile. The description adds the ranking criteria and the fact that reasoning accompanies each result, but omits notable traits: the mandatory x402 payment payload and the latency cost of a two-stage fetch-then-rank pipeline.

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 front-loaded sentences that lead with the core action and then explain the pipeline and output. No filler, though the second sentence is slightly verbose about ranking criteria.

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 shape needn't be documented, and annotations cover the safety profile. The remaining gap is that the description never signals the required x402 payment payload, which is a genuine prerequisite an agent must satisfy before calling.

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% across all three parameters, so the schema already documents search_term, num_videos (default 5, max 20), and the x_payment payload. The description adds no parameter-level detail, so the 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?

States a specific verb chain (search, fetch, rank) and resource (YouTube videos), and explains the two-stage mechanism (YouTube Data API discovery + Muse Spark AI ranking on relevance/credibility/quality). This clearly differentiates it from browsing-oriented siblings like trending_on_youtube, though it never names an alternative to route against.

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 (curated, ranked recommendations) but gives no explicit when-to-use guidance, no prerequisites, and no comparison to siblings such as trending_on_youtube, niche_research, or keyword_autocomplete. An agent must infer selection criteria entirely.

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 and destructiveHint=false, so the read-only safety profile is covered. The description adds that the tool analyzes hooks, pacing, and formats, but it does not disclose additional operational behavior such as payment handling, rate limits, or what the returned analysis looks like. No contradiction with annotations.

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, with the key idea in the first line and a clarifying sentence after it. The first clause is slightly redundant with the second, but overall it is scannable and free of unnecessary 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?

Given the presence of an output schema, full schema descriptions, and read-only annotations, the description supplies enough context: input is a topic, selection is by views, and output focuses on copyable Shorts patterns. It lacks explicit sibling routing, but the structured metadata covers most operational 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 description coverage is 100%, so all parameters—search_term, num_videos, and x_payment—are already individually documented. The description only echoes 'topic' as a synonym for search_term and does not meaningfully enrich parameter understanding.

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 identifies a specific resource (YouTube Shorts for a topic) and a concrete analysis goal: opening hooks, pacing, and repeatable formats. It also hints at the selection logic ('Top Shorts by views') and the intended audience ('a new creator can copy'), which separates it from sibling tools like shorts_to_script or get_video_intelligence, though it does not name those alternatives.

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

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

The description conveys the intended scenario: a creator researching a topic to find copyable Shorts formats. However, it never explicitly states when to prefer this tool over related siblings such as shorts_to_script, trending_on_youtube, or get_video_intelligence, and gives no exclusions.

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 and destructiveHint=false, covering the safety profile. The description adds some behavioral context—'fast, pure AI' and 'words + visuals + shot list'—which clarifies the nature and shape of output. It does not contradict the annotations, but it also doesn't disclose details like authentication 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 compact at two sentences with the core purpose front-loaded. The second sentence adds workflow context. Minor promotional phrasing like 'fast, pure AI' and 'winning patterns in, plug-and-play shoot sheet out' could be tighter, but there is little 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?

Given the presence of an output schema, rich annotations, and a fully documented input schema, the description covers the essential workflow: turning a topic and winning patterns into a script with visuals and shot list. It does not mention the required x_payment payload, but that is clearly described in the schema. Overall, the description is sufficient for an agent to understand the tool's role.

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 topic, x_payment, hook_style, and duration_secs with helpful descriptions. The tool description mentions 'from a topic' and output format but adds no meaning for the parameters themselves, so the 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 the tool's function: 'Shorts script writer: hook, beats, CTA from a topic' identifies both the resource (a short video script) and the core deliverable (hook, beats, CTA). It references shorts_intelligence for workflow context, which helps distinguish it from other content tools, though it doesn't explicitly name a sibling alternative.

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?

'Closes the loop with shorts_intelligence' and 'winning patterns in, plug-and-play shoot sheet out' imply that this tool is meant to be used after shorts_intelligence, using pattern data to generate a script. This is a useful context signal but does not explicitly state when to use it versus alternatives or when not to use it, so the usage guidance remains largely 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context by explaining that the tool reads the video's transcript and outputs both a click-worthy thumbnail spec and a ready-to-render image prompt. This goes beyond the annotations and clarifies the tool's non-mutating, analysis-oriented 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 short, front-loaded with the tool's purpose, and every sentence earns its place. It states the output categories, the input source, and the pipeline relationship without 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?

For a read-only, concept-generation tool with an output schema and well-covered parameter descriptions, the description is largely complete. It could be more explicit about needing video_url and the x_payment requirement, especially since the schema marks zero parameters as required, but the pipeline context and parameter descriptions compensate.

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 little per-parameter meaning beyond the schema; it implies video_url is used to fetch the transcript, but it does not discuss niche or x_payment. The schema descriptions themselves carry the 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 clearly identifies the tool's job: read a video transcript and produce a thumbnail concept with layout, overlay text, and a render prompt. It also explicitly frames itself as the 'text phase of thumbnail_creation,' which distinguishes it from the sibling tool thumbnail_creation.

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 by positioning this tool as the text phase of thumbnail_creation, so an agent can infer it should be used before the rendering/full thumbnail step. It does not explicitly state when-not-to-use or name other alternatives, but the pipeline framing is strong enough for correct selection.

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?

Adds valuable behavioral context beyond annotations: cost ($0.50), slow call up to 5 minutes, filter rejections not charged, server-side download rationale, and the use of Muse Image. No contradiction with annotations.

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?

Front-loaded with purpose and cost, then moves into latency, charging policy, and parameter guidance. Every sentence carries operational information; there is 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?

Covers cost, latency, payment, input alternatives, optionality, and transport preference. Since an output schema exists, return-value details are not needed. The only implicit point is that one of image_prompt/video_url must be supplied, but the alternatives make that clear.

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, but the description adds relational meaning between image_prompt/video_url and image_b64/image_url, including the optional-photo behavior. This goes beyond what the schema alone 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?

States a specific verb (render) and resource (YouTube thumbnail), and distinguishes itself from thumbnail_concept by describing the compositing step and the two input paths. An agent can tell it apart from sibling tools without opening their 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?

Gives clear input-mode guidance: pass image_prompt directly from thumbnail_concept or use video_url to derive the concept first, and photo is optional. It also advises preferring image_url over image_b64. However, it never explicitly says when not to use this tool or names an alternative for concept-only work.

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, idempotentHint, and openWorldHint, so the safety profile is covered. The description adds genuinely new behavioral context: the exact output shape (three ranked rewrites, a description draft, tags), the 60-char constraint, that it is 'pure AI' and 'fast', and that no payment or video lookup is involved. It does not contradict the annotations.

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?

Short, front-loaded, and each fragment carries information: identity first, then output shape, then the no-lookup precondition, then cost. The terse fragments read slightly like ad copy ('fast, pure AI', 'Free, no payment required') but nothing is wasted.

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 need not be re-explained, yet the description still summarizes what comes back, which helps selection. With annotations covering safety and a single required parameter, the definition gives an agent enough to invoke it correctly; only explicit routing against sibling tools is absent.

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

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

Schema description coverage is 100% and both parameters (title, niche) are documented in the schema, so the baseline is 3. The description's mention of 'keyword-front-loaded' hints at the niche/keyword-targeting purpose but adds no syntax, format, or constraint detail beyond what the schema already states.

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 a specific resource (YouTube titles and descriptions) and states the concrete output: three CTR-ranked rewrites under 60 chars, a keyword-front-loaded description draft, and tags. This is far more than a restatement of the name, and the output profile implicitly separates it from siblings like thumbnail_concept or transcript_markdown, though no sibling is named explicitly.

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 video lookup needed' tells the agent this works from a bare title, which is a useful precondition, and 'free, no payment required' removes a cost gate. However, there is no explicit when-to-use vs. alternatives guidance (e.g., versus niche_research or thumbnail_concept), so usage must be inferred.

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 read-only, idempotent, non-destructive, open-world behavior. The description adds useful context the annotations don't carry: output is timestamped, linkable Markdown with a metadata header, and it's free/no payment 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?

Three short sentences, front-loaded with the core action and output format, followed by the alternative routing. No wasted text.

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 to explain return values, annotations cover safety, and the description supplies output format, metadata header, and the sibling alternative. Nothing an agent needs to call it correctly 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?

Only one parameter, and schema coverage is 100%, so the schema already documents video_url / video ID fully. The description adds no format or syntax guidance beyond it, so the baseline 3 applies.

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

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

States a specific verb (download), resource (YouTube video transcript), and output format (Markdown file), plus an explicit exclusion ('no AI analysis'). This cleanly distinguishes it from get_video_intelligence and transcript_text 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?

Explicitly routes the agent to get_video_intelligence for AI summaries, which is the key alternative. However it never mentions transcript_text (a listed sibling), leaving the plain-text vs Markdown choice to inference.

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 cover read-only, idempotent, non-destructive, open-world. The description adds non-annotation context: it is free/no AI, returns raw captions with timestamps and 'nothing else'. It doesn't discuss failure modes (no captions, private video) or rate limits, so it falls short of a 5.

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

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

Front-loaded with the core capability and the two alternatives; every sentence carries routing information. Minor redundancy: 'free, no AI' is restated as 'Free, no payment required' at the end.

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 need not be explained, and the description covers capability, cost, output character, and sibling routing. Nothing an agent needs to invoke it correctly 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?

Schema description coverage is 100% and the single parameter is fully documented as 'YouTube video URL or video ID'. The description's 'any YouTube video' adds no syntax or format detail beyond that, so the baseline 3 applies.

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

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

Specific verb+resource+format: 'Fetch any YouTube video transcript as plain text.' It immediately distinguishes itself from two named siblings, so an agent can tell what it returns 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?

Explicit routing: transcript_markdown for a formatted file, get_video_intelligence for AI summaries. This is exactly the when-to-use-this-vs-alternatives guidance the dimension asks for.

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 openWorldHint=true, so the safety profile is covered. The description adds that a niche yields AI-scored picks and trend-riding suggestions, which is real behavioral context, but it omits the paid x402 requirement implied by x_payment — a notable gap for an agent that must supply payment to succeed.

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?

Front-loaded with the core action in sentence one, and only two short sentences plus a fragment. The second paragraph partly duplicates the first and drifts toward promotional phrasing ('ways to ride trends this week'), but the total is compact and easy to scan.

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 need not be explained, and all four parameters are schema-documented. The main omission is the mandatory payment payload, though the schema itself marks x_payment as required. For a low-complexity read-only fetch, this is nearly 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: niche, region, x_payment and num_videos are all documented in the schema, including defaults and the max of 15. The description restates the role of niche and region but adds no format or constraint detail beyond 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 opening sentence gives a specific verb+resource (fetch trending YouTube videos) plus the region and optional niche scoping. It distinguishes the tool from most siblings by being a trend-lookup rather than an analysis or generation tool, though it never explicitly contrasts with niche_research or analyze_youtube_topic.

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 implies usage ('pass a niche to get AI picks for what matters'), which tells the agent one conditional behavior, but gives no when-not guidance, no prerequisites, and no routing to the many adjacent analysis siblings. Usage is inferable but not 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?

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds useful behavioral context: 'One Data API call for stats', 'deterministic tier math', and that Muse justifies the tier and prescribes exactly 3 fixes. No contradiction with annotations.

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

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

The description is two tight sentences that front-load the core purpose and output. Every phrase earns its place, with no redundancy 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?

With an output schema present, return values need no explanation. The description covers the tool's single-call behavior, deterministic scoring logic, and the prescriptive nature of the output. It is complete enough for an agent to invoke the tool correctly.

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

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

Schema description coverage is 100%, so the input schema already documents both video_url and x_payment. The description does not add parameter-level meaning, which is acceptable given the baseline of 3 for full 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 states a specific action ('Score any YouTube video') and names concrete outputs: engagement rate, S/A/B/C tier, and 3 fixes. This clearly distinguishes it from sibling tools like get_video_intelligence or thumbnail_concept by focusing on scoring and prescriptive improvements.

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 'Score any YouTube video' implies broad applicability, but the description does not explicitly state when to prefer this tool over siblings or when not to use it. Usage context is implied rather than directly specified.

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 establish readOnly, idempotent, non-destructive and open-world access, so the safety profile is covered. The description adds genuinely non-structured context: the call is free/no-payment, the transcript is truncated to ~2000 characters, and no AI analysis is performed. That cap and the absence of analysis are the kind of limitations an agent needs before choosing this over the paid 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?

Front-loaded with the action and result, followed by two short lines that each carry distinct information (positioning vs. paid sibling, and the truncation/no-analysis cap). The third line is a telegraphic fragment list, which is dense but reads slightly clipped rather than fully polished.

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 shape need not be described, and the single parameter is fully documented. Combined with the cap disclosure, the no-payment fact, and the sibling routing, an agent has everything required to decide and invoke 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?

There is a single parameter and schema description coverage is 100%, so the schema already explains that video_url accepts a URL or bare video ID. The description adds nothing about accepted formats, ID-vs-URL handling, or what happens with the empty default. Baseline 3 is appropriate when the schema does all the work.

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

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

States a specific verb (fetch), a specific resource (free YouTube video preview) and enumerates the returned payload (title, channel, stats, transcript excerpt). It explicitly positions itself relative to the sibling get_video_intelligence, so an agent can tell the two apart without opening either 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?

Names the alternative explicitly ('Taster for get_video_intelligence') and gives the selecting condition (use before committing to payment), which is real routing guidance. It does not address other plausible siblings such as transcript_text or transcript_markdown, so it stops short of full when/when-not coverage.

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