List trending YouTube Shorts
youtube_shorts_trending_listList currently trending YouTube Shorts. Returns a list (use cursor when paginated).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
youtube_shorts_trending_listList currently trending YouTube Shorts. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and openWorldHint=true, covering safety and data scope. The description adds that it returns a list and mentions cursor-based pagination. However, the pagination hint is ambiguous because the input schema allows no parameters, so it is unclear how a cursor would be supplied. No contradiction with annotations, but the behavioral disclosure is incomplete.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences: 'List currently trending YouTube Shorts. Returns a list (use cursor when paginated).' It front-loads the core purpose and includes necessary operational detail without any filler words. 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?
For a zero-parameter tool, the description covers the primary action and notes pagination, but the pagination instruction is not actionable given the empty input schema, and there is no output schema to describe the returned list items. An agent may be uncertain about the response structure or how to actually use the cursor. The description is adequate but leaves these gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so schema description coverage is trivially 100%. Per the rubric, a baseline of 4 is appropriate because there is no parameter information for the description to add. The description does not need to elaborate on parameters that do not exist.
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 verb ('List') and resource ('currently trending YouTube Shorts'). It is unambiguous and the term 'trending' implies a platform-wide feed, distinct from channel-specific tools like youtube_channel_shorts_list. However, it does not explicitly name sibling alternatives, so it does not fully differentiate itself.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives such as youtube_channel_shorts_list, youtube_search_list, or trending tools on other platforms. The description simply states what it does without any exclusions, conditions, or alternative suggestions. An agent receives no help in selecting this tool over its siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.
All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).
The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.
The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.