youtube_video_comments_listList comments on a YouTube video by URL. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Link to the YouTube video whose comments should be listed. | |
| order | No | Comment sort order. `top` returns highest-ranked comments; `newest` returns the most recent comments. | |
| cursor | No | Opaque cursor from a previous response to fetch the next page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true, so the safety profile is already known. The description adds the pagination hint (cursor usage) but does not explain other behaviors like rate limits, completeness, or what the returned list contains. Given that annotations cover the core safety disclosure, a 3 is appropriate.
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 with no filler. The primary purpose is front-loaded, and the pagination note is efficiently placed. 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 simple list tool with full schema coverage, annotations, and a clear name, the description is nearly complete. It captures the essential behavior (returns a list, paginated via cursor) without needing to explain the output schema (none provided). Minor omissions like authentication requirements are not critical here given the read-only nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so all parameters are documented in the schema. The description adds minimal semantic value beyond clarifying the purpose ('by URL') which is already obvious. It does not elaborate on order or cursor semantics, but the schema already explains them. Baseline 3 fits.
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 action (list comments) and the resource (a YouTube video by URL). It does not explicitly differentiate from sibling tools like youtube_video_comments_analysis_get or youtube_video_comments_replies_list, but the name and description are unambiguous enough for a basic read operation.
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 a usage hint about cursors for pagination ('use cursor when paginated'), which is helpful. However, it does not mention when to prefer this tool over the analysis or replies siblings, or any exclusions or prerequisites. The usage context is implied rather than explicit.
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.