ScrapeMole

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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses a lot the structured fields cannot: the return shape, that errors are not charged, that this is a paid x402/USDC-on-Base tool at $0.01 per successful call, and the practical payment handshake (call without payment to receive the PaymentRequired object). This is exactly the operational context an agent needs before invoking a metered endpoint.

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, each earning its place: use trigger, return contents, then billing/error semantics. The 'Use when' clause is front-loaded, which is the right ordering for tool selection.

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

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

There is no output schema, but the description compensates by naming what comes back (0-100 interest series, regional breakdown, top/rising queries). Combined with a fully documented 5-parameter schema and clear pricing/error rules, an agent has everything needed to call this 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% and the enums, defaults, and per-parameter descriptions already define q, geo, time, regions, and related fully. The description's mention of per-region interest and related queries lightly echoes the regions/related flags but adds no syntax, format, or comparison guidance beyond the schema, so the baseline 3 applies.

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

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

The description opens with a concrete use condition ('how search interest in a topic changes over time or by region, or which related queries are rising') and names the exact resource (Google Trends). The platform-specific resource makes it trivially distinguishable from the youtube_* and amazon_product siblings, and the return shape (weekly/daily interest 0-100, per-region, related queries) is stated up front.

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 gives a clear 'use when' trigger tied to the two analysis needs the tool serves (trend-over-time/region and rising related queries). It does not name when-not-to-use cases or point to an alternative sibling, so it falls short of the explicit-alternatives bar, but the triggering context is unambiguous.

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, openWorld and idempotent, and the description goes well beyond them: failed calls are not charged, the tool is paid via x402 USDC on Base at $0.002 per successful call, and a payment-less call returns a PaymentRequired object. That is exactly the billing and failure behavior an agent needs before invoking a paid endpoint.

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: use case first, accepted inputs second, payment model last. Every clause carries information (payload fields, input formats, price, error-charging policy) and nothing is repeated from the annotations.

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

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

With no output schema, the description compensates by enumerating the returned fields (views, duration, approximate publish date) and the volume cap, and it covers the paid-invocation flow. An agent has everything needed to call it correctly and interpret the result.

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 channel, limit and tab are already documented in the schema. The description restates the accepted channel identifier formats (@handle, URL, UC… ID) and the 50-item cap but adds no new syntax or semantics, so the baseline 3 applies.

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

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

The description names a specific resource (a YouTube channel's published content) and the concrete payload returned: up to 50 latest videos, shorts or streams with views, duration and approximate publish date. This clearly separates it from siblings like youtube_video, youtube_playlist, youtube_search and youtube_comments without opening any 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?

"Use when you need what a YouTube channel has published" gives an explicit triggering condition, and the tab enum is described as which feed to pull, implying the selection logic. It stops short of naming when to prefer a sibling (e.g. youtube_search for cross-channel queries), so 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?

Adds substantial context beyond the readOnly/idempotent/openWorld annotations: a hard cap of 100 top-level comments, exclusion of commenter names/handles/channel IDs, the fact that comments-off videos and errors are not charged, the x402 USDC-on-Base price per successful call, and the PaymentRequired object returned when called unpaid. This is exactly the payment and authorization information annotations cannot convey.

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 trigger condition before the output inventory, pricing and error semantics, so the highest-value information lands first. Dense but nearly every clause carries load; only the field enumeration is slightly list-heavy.

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

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

With no output schema, the description carries the full return-value burden and does so: it names the returned fields, the volume cap, the deliberate omission of commenter identity, and the total count. Pricing, non-charging behavior and the PaymentRequired fallback cover the remaining gaps for a paid open-world tool.

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

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

Schema description coverage is 100%, so the schema already documents lang, sort, limit, video and country – the baseline for this dimension. The description corroborates the limit range ('up to 100 top-level comments') and clarifies that replies are not returned, but adds no syntax or format meaning for the remaining parameters.

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 resource (top-level YouTube comments on a video) and enumerates exactly what comes back: text, like count, reply count, relative publish time, pinned/by-creator flags, total comment count. An agent can distinguish it from youtube_video, youtube_transcript and youtube_channel without opening any 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?

Opens with an explicit trigger ('Use when you need what viewers say about a YouTube video') and lists concrete use cases such as sentiment, feedback mining, FAQ and product research. It stops short of naming alternatives (e.g. use youtube_transcript for spoken content), so routing across siblings still requires 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 the safety profile (readOnly, idempotent, openWorld), and the description adds material context beyond them: a paid x402 USDC/Base flow at $0.002 per successful call, the pay-without-payment handshake that returns a PaymentRequired object, and the billing guarantee that private/missing playlists and errors are not charged. That is exactly the economic and failure behavior an agent needs to plan a call.

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 tight sentences: scope first, input formats second, pricing/error semantics last. Every clause carries information an agent would otherwise have to guess, and nothing is redundant with the name or annotations.

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

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

With no output schema, the description correctly enumerates the return shape (per-video fields plus playlist title/description/total count), and it covers payment mechanics, error billing, and input formats. Nothing needed to invoke or budget the call 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 coverage is 100%, so baseline is 3, and the description adds value by naming the accepted ID shapes (PL…, UU…, OL…) that the schema's generic 'playlist URL or ID' does not specify. It also restates the 100-video ceiling for limit, though lang/country remain purely schema-documented.

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 ('contents of a YouTube playlist') and enumerates what is returned (videos with title/channel/duration/thumbnail plus playlist metadata), which cleanly separates it from youtube_channel, youtube_video and youtube_search. An agent can select it 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?

The opening 'Use when you need the contents of a YouTube playlist' gives a clear triggering condition and scope examples (course, series, music, channel uploads), which implicitly disambiguates it from sibling video/channel tools. It stops short of explicitly naming an alternative tool for the adjacent case (e.g. fetching a single video vs a playlist).

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?

Goes well beyond the readOnly/idempotent/openWorld annotations by disclosing the return payload shape (title, channel, views, duration, publish date, thumbnail), the 50-video cap, the flat pricing model, that errors are not charged, and the x402 USDC-on-Base payment flow including the PaymentRequired handshake for unpaid calls. This is exactly the auth/payment context an agent needs and cannot get from structured fields.

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 use case and packs return fields, filters, pricing, and payment mechanics into a compact block with no filler. Sentences are dense and slightly run-on, but nothing is wasted and the most decision-relevant information (paid, $0.002, PaymentRequired) is easy to find.

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

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

With no output schema, the description compensates by enumerating returned fields, and it fully covers the non-obvious commercial/payment prerequisites. For a read-only search tool, 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%, so the schema already documents all seven parameters. The description restates three filter categories (sort order, upload date, duration) and the 50-video ceiling but adds no syntax, format, or behavioral nuance beyond the schema, 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+resource ('find YouTube videos for a query') and adds a use-case framing ('competitor or trend research') that helps an agent recognize the scenario. It does not explicitly name or contrast with siblings like youtube_video or youtube_channel, so an agent must infer that this is query-based discovery rather than ID-based 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 a clear when-to-use trigger ('when you need to find YouTube videos for a query') plus concrete example scenarios. No when-not conditions or named alternatives are provided, so it stops short of full routing guidance.

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

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

Goes well beyond the readOnly/openWorld/idempotent annotations by disclosing billing behavior (flat $0.002 per successful call, any limit up to 50), error handling (errors are not charged), the free-trial window (first success per client IP per 30 days, signalled by X-ScrapeMole-Free-Trial: 1), and what happens on later calls (402 with a PaymentRequired object). This is exactly the operational context annotations cannot carry.

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 use case and return payload are front-loaded, and the free-trial mechanic follows logically. There is some redundancy, though: the $0.002 price and the paid-call behavior appear twice, and the free-trial and paid flows could be merged into one tighter sentence.

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 paid, no-output-schema tool it covers what's needed: the returned field set, the 50-item ceiling, filter dimensions, billing, error non-charging, and the exact 402/free-trial handshake. Minor gaps remain around response shape details (e.g., what a 402 body contains beyond the mention of PaymentRequired) and whether the trial flag appears on every response.

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 a 3 is the baseline, but the description adds real meaning: the optional filters correspond to sort order, upload date and duration, and pricing is flat regardless of the limit chosen up to 50. That clarifies the practical cost implication of the limit parameter, which the schema does not state.

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 ('find YouTube videos for a query') and lists the returned fields and filter dimensions, so the agent knows exactly what it gets. It does reference the paid endpoint '/v1/youtube/search', but never names the sibling tool youtube_search or states how the free-trial call differs operationally from it, so sibling differentiation is only implied.

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?

'Use when you need to find YouTube videos for a query, e.g. competitor or trend research' gives a concrete usage context, and the payment mechanics ('call with a query and no payment header') tell the agent precisely how to invoke the free trial. It stops short of explicit routing rules, such as 'after the trial is consumed, use youtube_search instead'.

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?

Goes well beyond the readOnly/openWorld/idempotent annotations: manual captions preferred with auto-generated fallback, optional translation, no charge for videos lacking captions or on errors, and the x402 USDC payment model at $0.002 per successful call including the 'call without payment to receive the PaymentRequired object' flow. This is exactly the extra behavioral context annotations cannot carry.

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 'Use when' trigger, followed by the payment/pricing caveat. Dense but every clause (captions fallback, translation, non-charging, price, PaymentRequired) carries actionable information. Slightly long as a single run-on first sentence.

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

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

With no output schema, the description still characterizes the return shape (full transcript text plus timestamped segments) and covers the failure/payment paths an agent must handle. Nothing material for correct 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 coverage is 100%, so baseline is 3, but the description adds real semantics: language is a preference with manual-captions-first / auto-generated fallback resolution, and translation only applies when the video lacks the requested language. These clarify how lang and translate interact beyond the schema's field-level text.

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

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

States a specific verb+resource outcome: 'full transcript text plus timestamped segments, in the requested language'. An agent immediately knows this returns spoken content rather than metadata. It does not explicitly distinguish itself from siblings like youtube_video or youtube_search, so it falls 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?

'Use when you need what is said in a YouTube video' gives a clear triggering condition, and the caption fallback/translation notes clarify expected behavior. However, no alternative sibling is named as a contrast (e.g. 'for title/description metadata use youtube_video'), so routing between the seven YouTube siblings is left 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 declare readOnly, idempotent and openWorld, but the description adds genuinely new operational behavior: the paid x402 USDC-on-Base model at $0.002 per successful call, the instruction to call without payment to receive the PaymentRequired object, and the no-charge policy for unavailable/private videos and errors. These are things an agent cannot learn from the annotations or 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?

Three tight sentences ordered by importance: use case and returned fields first, accepted input formats second, billing/error behavior last. No filler or repetition of the tool name.

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

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

With no output schema, the description appropriately enumerates the returned fields, and the annotations cover the safety profile. Payment requirements, accepted input formats, and failure/charging behavior round out everything an agent needs 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema by accepting 'a video URL, Shorts URL or 11-character ID' — the schema only mentions 'YouTube video URL or 11-character video ID', omitting Shorts. It adds nothing for lang/country, which the schema already documents.

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 ('the details of one YouTube video') and enumerates the exact data returned (view/like/comment counts, duration, publish date, category, description, tags, live status, thumbnail). The 'one YouTube video' scope cleanly separates it from siblings like youtube_channel, youtube_comments, youtube_playlist and youtube_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?

'Use when you need the details of one YouTube video' gives clear selection context, and the field enumeration reinforces that. However, it never names the alternatives (e.g., use youtube_search to find a video, youtube_transcript for captions) or states when not to use it, so routing still requires inference.

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