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

No annotations are provided, so the description carries the full behavioral burden and does so well: it discloses cost ('one unit from the shared pool'), the error shape for missing handles (error vs. empty result), the hidden_subscriber_count edge case that makes subscriber_count meaningless, and the rounding of subscriber counts. These are all non-obvious behaviors an agent needs.

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 operation and the core constraint, then useful details. Every sentence earns its place, though it runs long and could be tightened. The parenthetical statistics list is efficient.

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-value explanation is not required, yet the description still calls out key return fields and edge cases (hidden subscriber counts, meaningfulness of subscriber_count). Combined with the identifier contract and cost disclosure, an agent has everything needed to call 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 0%, so the description must compensate and it does: 'handle' accepts '@name' or 'name', and the exactly-one-of-two constraint is stated. This fills the schema gap fully for both 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 verb and resource ('Get a YouTube channel') and immediately constrains the two input modes (by ID or by handle). It also disambiguates against the sibling youtube_list_channel_videos by naming it and clarifying that uploads_playlist_id is the input that sibling resolves. An agent can route correctly 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?

The description explicitly requires 'pass exactly one of the two' identifier modes, which is concrete selection guidance. It stops short of stating when to prefer this tool over youtube_search_videos for channel discovery, so it's clear on the input contract but not on the wider when/when-not decision.

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

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

With no annotations, the description carries the full burden and does so: it discloses the shared 10,000-unit quota cost per page, the 100-reply page cap, next_page_token pagination, plainText formatting, and that each page costs one unit. It also flags failure behavior for an unknown comment_id ('fails with a not-found error; retrying will not help') and warns that reply content is untrusted user input — unusually rich operational context.

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

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

Front-loaded with the core action and the ID provenance before quota, pagination and safety notes; each sentence carries distinct information. It is longer than typical, but the density is high and little is wasted — only the return-field restatement overlaps with the output schema.

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

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

Given three parameters, an output schema (so return shape needn't be re-explained), and no annotations, the description covers the gaps that matter: ID provenance, quota/pagination economics, error semantics, and content trust. Nothing needed 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 0%, so the description must compensate, and it does for the critical parameter: comment_id is pinned to a top-level comment ID from youtube_get_comments items[].comment_id and explicitly excluded from being the video ID or a reply's own ID. page_token is implied by the next_page_token mention and max_results' 100 ceiling is echoed ('at most 100 replies'), but the default of 20 and page_token's exact usage are left to the schema.

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

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

States a specific verb+resource+scope: 'Get the full set of replies to one top-level comment', with parenthetical return fields. It explicitly distinguishes itself from the sibling youtube_get_comments by explaining that tool's embedded replies sample is truncated, so an agent can pick correctly 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 selecting condition ('This is the tool for reply text' vs. the truncated sample in youtube_get_comments) and gives an explicit when-not for pagination ('page only if you need the tail'). It also routes the agent to the thread's total_reply_count for sizing, leaving no inference about alternatives.

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

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

With no annotations, the description carries the full burden and does so well: it discloses quota cost ('one unit from the shared pool'), page-token pagination, the API-truncated reply sample (at most 5), the disabled-comments failure, plaintext rendering, and an untrusted-input warning. It stops short of stating auth requirements or whether the call is read-only, but mutation is clearly not implied.

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 verb and scope, then layered detail in short paragraphs. The reply-count point is restated three ways (not len(replies), count to quote, never present the sample as the whole conversation), which is emphasis but slightly redundant.

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

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

An output schema exists, yet the description still frames the meaningful return fields and explicitly clarifies the one field agents most often misread (total_reply_count vs. sample size). Nothing needed to call or interpret this tool 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 coverage is 0%, so the description must compensate, and it explains `order` (time/relevance with default), `page_token` (via returned next_page_token), and `include_replies` in detail. `max_results` and `video_id` are never described, leaving two of five parameters undocumented in both schema and prose.

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?

Opens with a specific verb and resource ('Get a video's **top-level** comments') and immediately enumerates the returned fields, making the top-level scope explicit against reply-fetching siblings. An agent can distinguish this from youtube_get_comment_replies 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?

Gives explicit routing: use this tool for top-level comments, and call youtube_get_comment_replies with a comment_id for the full reply set. It also documents ordering choices and pagination flow, plus the disabled-comments failure case where retrying is pointless.

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

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

With no annotations, the description carries the full burden and does so: it discloses the quota cost (one unit from the shared 10,000-unit pool), the error taxonomy (nonexistent vs private fail differently, neither is retryable, private stays private with an API key), and the semantic edge case that item_count 0 is a real empty playlist rather than a failure.

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 return shape, then prerequisites and failure modes. Dense and mostly earned, though the aside about `search.list`'s 100 calls/day bucket is slightly beyond what's needed to invoke this tool correctly.

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

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

For a single-param getter with an output schema, nothing an agent needs is missing: purpose, prerequisite, sibling routing, quota cost, and failure semantics are all present. The return-field discussion is somewhat redundant with the output schema but reinforces the playlist-vs-video timestamp distinction.

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

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

Schema coverage is 0% and the single `playlist_id` param is undocumented in the schema, so the description must compensate. It explains what the ID must be and where to obtain it (channel `uploads_playlist_id`, video page, user), but stops short of giving the ID's string form or an example, leaving the agent to infer format.

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 with explicit scope: "Get one playlist's own metadata by ID", then enumerates the fields returned (title, description, channel, size, visibility). It explicitly separates itself from the sibling that handles playlist contents by naming `youtube_list_playlist_items`.

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 explicit when-to-use routing ("For the videos in it, use youtube_list_playlist_items"), a hard prerequisite ("Requires the playlist ID — there is no lookup by name"), and explains why discovery is not offered. It even tells the agent where to source the ID (`youtube_get_channel` uploads_playlist_id, video page, user).

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

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

With no annotations, the description fully carries the behavioral burden and does so unusually well: it states that the call costs no quota, reads an in-process counter, cannot fail with `quotaExceeded`, reports zero buckets rather than refusing, and that numbers are approximate and process-local. It also warns that Google may still refuse a call the counter says is affordable, covering the main failure-model gap.

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 well structured and front-loads the core purpose, then moves through usage, cost, and caveats. It is a bit long for a zero-parameter status tool, and the bucket return-field explanation partly duplicates the output schema, but the caveats are substantive rather than filler.

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

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

For a zero-parameter quota-status tool with an output schema and no annotations, it covers what an agent needs: what is returned, when to check it, which alternatives to prefer, that the call is free, and why the numbers may be inaccurate. Nothing material is missing for correct invocation.

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

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

There are zero parameters, so the baseline is 4. No parameter description is needed, and the schema is trivially complete.

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

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

The description opens with a specific verb and resource: reporting today's remaining YouTube Data API quota and its reset time. This is unambiguous and clearly distinct from every search/retrieval sibling, which it explicitly references as 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?

It says exactly when to use the tool: before a deliberate search, especially when the scarce `search` bucket may be low. It names concrete alternatives with their quota costs (`youtube_list_channel_videos`, `youtube_search_in_transcript`) and frames `youtube_search_videos` as scarce and deliberate.

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

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

No annotations exist, and the description carries the full burden well: it discloses truncation at RESPONSE_LIMIT with a cursor continuation model, that the cost is zero Data API quota, that failure modes match youtube_get_transcript, and — importantly — that an untranslatable target language is a permanent INVALID_REQUEST rather than a retryable error. It also flags that translated words are YouTube's machine translation rather than creator speech, with provenance fields is_translated/translated_from.

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 purpose and return shape, then organized into translation, pagination, and cost/failure paragraphs. Dense but nearly every sentence adds decision-relevant information; the long-form prose is justified for a tool with no annotations and rich semantics, though it runs longer than strictly necessary.

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 the description needn't document return fields, yet it explains them anyway. Pagination, truncation, quota cost, and error classification are all covered. The gaps are the undocumented `languages` parameter and the absence of any note on authentication or per-video availability restrictions.

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

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

Schema coverage is 0%, so the description must compensate. It does so thoroughly for translate_to (valid targets, provenance, error behavior) and cursor (index of next segment, null means done), and implies video_id via the deep-link example. However, the `languages` array parameter is never mentioned in either the description or the schema, leaving one of four parameters entirely opaque.

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 YouTube video's transcript as timed segments') and immediately differentiates from the sibling youtube_get_transcript. The return shape {text, start, duration} plus the translation variant makes the tool's identity unambiguous.

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

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

Explicit routing: 'Use this to build chapter lists or deep links; use youtube_get_transcript when you only need the words.' It also names the when-for-translation case and points at youtube_list_transcript_languages as the source of valid targets. Exclusions and alternatives are both present.

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

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

With no annotations, the description carries the full burden and does: no Data API quota cost, forever-caching with separate keys for translated pages, truncation at `RESPONSE_LIMIT` on caption boundaries, and a specific failure taxonomy (captions disabled, absent language, transient IP block, INVALID_REQUEST, age-restricted). It also warns the returned text is YouTube's machine translation and may be wrong.

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 organized into translation, paging, and cost/failure blocks — each paragraph has a job. It runs long, and the `is_translated`/`translated_from`/`is_generated` explanation partly restates return fields that an output schema already covers.

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 5-parameter scraping tool with no annotations and an output schema, this covers the paging loop, truncation anchor, cache behavior, quota impact, and error handling. An agent has everything needed to call it correctly and interpret a failed call.

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

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

Schema coverage is 0%, so the description must compensate and largely does: `video_id` is one 11-character ID, `cursor` is what you pass back from `next_cursor`, `include_timestamps` trades tokens for `[mm:ss]` markers, and `translate_to` targets the track selected by `languages`. What remains implicit is how `languages` is resolved when several are supplied (preference order vs. first available).

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?

Opens with a specific verb and resource: "Fetch a YouTube video's transcript as plain text, optionally translated," scoped to a single 11-character video ID. It differentiates on timestamps (off by default) but never names the obvious sibling `youtube_get_timestamped_transcript`, leaving the agent to infer the split.

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 concrete operating instructions: get valid `translate_to` targets from `youtube_list_transcript_languages`, page with `next_cursor` until it is null, and do not retry an `INVALID_REQUEST` language error. It stops short of stating when to prefer this over the timestamped-transcript sibling.

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

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

With no annotations, the description carries the full burden and does so: it discloses quota cost (one unit per 50 IDs from a shared 10,000-unit daily pool), that a separate tool uses a different bucket, that parts increase response size but not quota, and that dislike counts are permanently unavailable. These are exactly the behavioral facts an agent cannot infer from the schema.

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

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

Front-loaded with purpose, then usage, then parameter guidance, and each sentence carries unique information (quota, missing IDs, part semantics, dislike caveat). It is slightly long at three paragraphs, but nothing is padding.

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 detail is not required, yet the description still explains the one non-obvious return behavior (missing_video_ids). Combined with quota, batching, and part semantics, an agent has everything needed to call this correctly and interpret results.

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

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

Schema description coverage is 0%, so the description must compensate, and it does: it explains that video_ids accepts a single string or an array of up to 50, and that parts selects snippet/statistics/contentDetails/status with a gloss for each enum value. This adds meaning well beyond the bare schema types.

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 ('Get YouTube video metadata by ID') and enumerates the returned fields (title, description, channel, duration, statistics). It also explicitly separates itself from the sibling youtube_get_video_stats, so an agent can route correctly 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?

Gives explicit when-to-use-elsewhere guidance ('If you only need view/like/comment counts, use youtube_get_video_stats instead') and explains how to interpret the missing_video_ids result rather than treating it as a failure. It also states the batch limit (one ID or up to 50) that governs how to call it.

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

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

No annotations are provided, so the description carries the full burden and does so: it discloses the separate quota bucket, cache TTL (~3600s and the 0-means-forever semantics), the non-atomic batch behavior with failed_video_ids, and the fact that dislike_count is unavailable and must not be worked around. These are exactly the operational traits an agent needs beyond 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-loaded with the core purpose and the key quota insight, and every sentence carries new information. It is dense and slightly long with nested parentheticals about cache semantics, but little of it is filler.

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

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

An output schema exists, so the description needn't restate return values, and it goes further by explaining the partial-success shape (failed_video_ids alongside successful results) and the cached flag semantics. Nothing an agent needs to call this correctly and interpret the response appears to be 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 0%, so the description must compensate, and it does: it explains the parameter accepts either a single ID or a list and enforces an 'up to 50 videos' cap that the schema does not convey. It does not specify ID format (e.g., raw ID vs URL), leaving a minor gap.

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 ('Get view, like and comment counts') plus the scope ('up to 50 videos in one call' and 'one ID or a list'). It explicitly contrasts itself with the sibling youtube_get_video, so an agent can distinguish it 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 (youtube_get_video) and gives the exact condition that selects this tool: it uses its own 10,000-call/day bucket and 'does not consume the shared pool that youtube_get_video draws on, so it is the cheap way to get numbers.' Clear when-to-prefer guidance with the rationale.

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

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

With no annotations, the description carries the full burden and does so well: it discloses no paging (whole list in one response), quota cost (one unit from the shared pool), response language (English titles), and the semantic wrinkle that some categories are assignable:false yet still valid as search filters. These are non-obvious operational facts an agent could not infer otherwise.

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 primary purpose leads, followed by practical notes in a logical order; each sentence adds distinct information. The assignable:false sentence is slightly tangential to invocation but still earns its place by preventing misuse of the returned data.

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

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

For a single-optional-parameter listing tool, everything an agent needs is present: scope, parameter format, default behavior, quota implication, and the downstream handoff to youtube_search_videos. Output schema exists, yet the description still usefully summarizes the returned fields.

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

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

Schema coverage is 0%, so the description must compensate, and it does: region_code is defined as ISO 3166-1 alpha-2 with concrete examples (US, PT), and the omission default is spelled out (YouTube picks based on the server's location). This fully replaces the missing schema description.

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

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

The opening sentence gives a specific verb and resource ('List YouTube's video categories') plus the optional scoping dimension (region). It is instantly distinguishable from siblings like youtube_search_videos or the transcript tools, since none of them enumerate the category taxonomy.

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 explicitly explains the downstream use case: pass the returned category_id to youtube_search_videos as video_category_id to filter by topic. That is a clear routing signal. It does not, however, state when NOT to use this tool or contrast it against other listing tools, so it stops short of a full 5.

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

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

With no annotations, the description carries the full burden and does so: it discloses the quota economics (1 unit per page of 50 from a shared 10,000 pool vs. the 100/day search bucket), caching behavior ('cached forever'), the pagination/stop-at-max_results semantics, and what the results omit (no view counts) plus where to get them. That is unusually rich operational context for a read 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 one-line purpose and cost framing, then layered detail. It is on the long side and the quota discussion is dense, but nearly every sentence adds actionable information; only the next_page_token remark is wasted due to not matching the schema.

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

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

Covers prerequisites, cost, pagination stops, and result contents, and an output schema exists so return-format detail is not required. The one real gap is the mismatch between the described continuation token and the actual parameter list, which could leave an agent unsure how to page.

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

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

Schema coverage is 0%, so the description must compensate and largely does: it specifies that channel_id is an ID rather than a handle and how to obtain it, and explains that max_results terminates the walk. However, it references `next_page_token` as the continuation mechanism while the schema exposes no such parameter (additionalProperties: false), leaving an ambiguity about how to fetch subsequent pages.

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 ('List a channel's recent uploads, newest first') and immediately positions itself against two siblings by name. An agent can distinguish it from youtube_search_videos and youtube_list_playlist_items 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?

Gives an explicit when-to-use rule ('whenever the question is "what has this channel posted"'), names the alternative to avoid (youtube_search_videos), and names the sibling to use for the adjacent case (non-uploads playlists or known playlist ID). It also states the prerequisite (channel ID, or start from youtube_get_channel with a handle).

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

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

With no annotations, the description carries the full burden and does so: quota cost of one shared-pool unit per page (10,000 shared vs the 100/day search bucket, which it does not touch), empty playlist returns `items: []` without error, private-playlist errors are distinct from not-found and are not retryable. It also clarifies that `published_at` is playlist add-date, not video publish date — a genuine semantic trap.

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 purpose and alternative, and nearly every sentence carries operational information. Slightly over-rich in asides (the parenthetical about the transcript cursor mechanism), and the markdown emphasis is noisy, but nothing is truly filler.

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

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

An output schema exists, yet the description still names the item fields and their meaning, covers quota, pagination, and both empty and private-playlist edge cases. An agent has everything needed to call it correctly and interpret failures.

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

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

Schema coverage is 0%, so the description must compensate, and it does for two of three params: `page_token` is described at length as an opaque `nextPageToken` never a page number and explicitly not the transcript character cursor; `playlist_id` sources are enumerated. `max_results` (default 50, max 50) is never mentioned, leaving the page-size control undocumented in both places.

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?

Opens with a specific verb and resource ('List the videos in *any* playlist, in playlist order') and immediately names the sibling it is not (`youtube_list_channel_videos`), explaining that no channel is required. An agent can distinguish this from every sibling 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?

Explicitly routes the agent: get `playlist_id` from `youtube_get_channel`'s `uploads_playlist_id`, `youtube_get_playlist`, a video's page, or the user. It also names the alternative for the channel-scoped case and points to `youtube_get_video_stats` for view counts this tool does not return.

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

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

No annotations exist, so the description carries the full burden and does: it discloses caching ('Cached forever, costs no Data API quota') and the failure mode ('Fails cleanly when a video has captions disabled or no caption track at all — that is a normal outcome, not an outage'). It also warns that generated tracks are less accurate than uploaded ones.

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 purpose in the first sentence, then covers return shape, usage routing, and reliability constraints without waste. Every sentence 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?

An output schema exists, so the field-level return documentation is a bonus rather than a necessity, and the operational caveats (quota, caching, clean failure) round out everything an agent needs to call and interpret this 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?

One parameter at 0% schema description coverage, so the schema provides no help. The description implies the input is a video identifier ('for a YouTube video') but never states the expected format or how to obtain the ID. Minimum-viable rather than compensatory.

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 (List) plus resource (caption tracks available for a YouTube video), and it names the entry fields returned. An agent can distinguish this from youtube_get_transcript, which fetches a transcript rather than enumerating tracks.

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 states when to use it: 'Use this before youtube_get_transcript when you need a specific language, or to tell the user which languages exist.' It names the alternative tool and the condition that selects it, leaving nothing 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?

With no annotations provided, the description carries the full burden and does so richly: server-side case-insensitive substring matching, per-segment matching limitation, literal-not-regex semantics, pagination via cursor with next_cursor:null signaling the last page, language default behavior, and 'costs no Data API quota; cached.' This is exactly the behavioral disclosure a mutation-free search tool needs.

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 one-line purpose, then layers limitations, then parameter/pagination notes. Despite its length every sentence adds distinct, actionable information (matching semantics, token savings, quota, caching) rather than restating the 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?

Although an output schema exists (so return shape need not be explained), the description still clarifies the match payload (start seconds, total_matches, up to 20 per page), pagination, and matching limitations. Combined with the schema, an agent has everything needed to call and interpret 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 description coverage is 0%, so the description must compensate, and it does: it explains `query` is a literal substring (not boolean/regex), `cursor` enables paging past 20 matches, `next_cursor: null` marks the end, and `language` selects the caption track with a default. Only `video_id` is left to the obvious name, which is acceptable.

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: 'Search inside a video's transcript and return only the matching segments.' It clearly contrasts with the sibling youtube_get_transcript by emphasizing it returns only matches rather than the full transcript, letting an agent distinguish it 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?

Gives a clear usage context with a concrete example ('does this video mention Kubernetes?') and frames the cost tradeoff against pulling the whole transcript, implicitly routing away from youtube_get_transcript. It does not name the alternative sibling explicitly, so it falls just short of 5.

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

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

No annotations are provided, so the description carries the full burden and does so: it discloses the scarce 100-calls/day quota bucket, that each page including page_token fetches costs another call, that results carry no statistics (and points to youtube_get_video_stats with its own 10,000/day bucket), and that dislike counts are unavailable since 2021. This is exactly the operational context an agent needs to avoid burning quota.

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 the quota warning, then structured into scannable segments for ordering, date filters, and safe_search. It is long, and a few sentences (e.g. restating the page-cost point) could be tightened, but nearly every line earns its place given the 9 undocumented parameters.

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 9-parameter search tool with 0% schema coverage, no annotations, and an output schema, the description supplies quota economics, safer alternatives, enumerations, timestamp format constraints, and the discovery that only like counts (not dislikes) are retrievable. Nothing essential to 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 0%, so the description must compensate, and it does for most parameters: order enum values plus the caveat about incomplete result sets, published_after/before as timezone-aware RFC 3339, safe_search enum and default, region_code ISO format, and video_category_id sourcing. It does not explain max_results or query semantics, so it falls short of fully covering all 9 parameters, but the coverage is well above baseline.

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

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

States a specific verb+resource ('Search YouTube for videos by keyword') and immediately distinguishes itself from siblings by naming how it differs from youtube_list_channel_videos and youtube_search_in_transcript. An agent can identify the tool's role 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?

Explicitly tells the agent to prefer other tools first, explains why (scarce 100/day quota), and routes to concrete alternatives with their cost profiles: youtube_list_channel_videos for channel listings and youtube_search_in_transcript for questions about a video. Names the precise condition ('when you genuinely need to discover videos by topic') that should select this tool.

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