youtube_video_comments_analysis_getAnalyze YouTube video comments. Accepts a video URL.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Link to the YouTube video whose comments should be listed. | |
| order | No | Comment sort order to analyze. Defaults to `top` for the strongest signal. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true, so the agent knows the operation is safe and may reference external data. The description adds no behavioral context beyond a vague 'Analyze'—it doesn't disclose what the analysis entails, whether it fetches all comments or a sample, any rate-limit considerations, or what the response contains. Since annotations already cover read-only, the description contributes little.
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 (two sentences) and non-repetitive overall, but the second sentence 'Accepts a video URL' is redundant given the schema already specifies a required 'url' parameter. It is concise but not front-loaded with the most distinguishing info, and the word 'Analyze' carries the entire burden of meaning without elaboration.
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 that claims to 'analyze' comments, the description is incomplete: it does not specify what analysis is performed, what the output looks like, or how the 'order' parameter influences the analysis. There is no output schema to clarify return values, so the description should compensate but doesn't. The agent cannot predict what the tool actually does beyond retrieving comments and calling it 'analysis.'
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%: both 'url' and 'order' have clear descriptions in the schema. The description only repeats that a URL is accepted, adding no new meaning about parameter formats, defaults, or constraints. The baseline of 3 applies because 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?
The description states the verb 'Analyze' and resource 'YouTube video comments', which is clear at a high level. However, 'analyze' is vague—it doesn't specify what kind of analysis (sentiment, spam, engagement, etc.) or how it differs from sibling tools like youtube_video_comments_list, which just lists comments. The name itself is more informative than the description in distinguishing this as an analysis tool.
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_video_comments_list or youtube_video_comments_replies_list. The description does not mention any prerequisites, what type of analysis is performed, or when 'analysis' would be preferred over a plain comment listing. The agent is left to infer usage 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.
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.