hackernews_story_comments_listList comments on a Hacker News story by id. Returns a list (use cursor when paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Hacker News story id whose comment tree should be returned. | |
| limit | No | Maximum comment nodes to return in this page (1–100). Default: 50. | |
| cursor | No | Opaque continuation cursor from a previous response. Omit for the first page. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description doesn't need to re-state safety. It adds value by noting the result is a list and that pagination via cursor is supported. This directly informs callers about the return type and how to navigate multiple pages, which is beyond the annotation coverage.
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 sentences with zero waste. It front-loads the core purpose and includes the key operational hint about pagination. Every sentence earns its place, making it easy for an agent to parse quickly.
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?
The tool has no output schema, so the description bears the burden of explaining the return value. It states 'Returns a list' but does not describe the fields or structure of the comment items, nor any error behavior. Given the simplicity of the operation and the schema's detailed parameter descriptions, it is adequate but could be more informative about the actual list contents.
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 id, limit, and cursor thoroughly. The description adds only marginal emphasis ('by id', 'use cursor when paginated') which partly reinforces but does not significantly extend the schema. Baseline 3 is appropriate as the schema carries the semantic load.
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 and resource: 'List comments on a Hacker News story by id.' This precisely distinguishes it from siblings like hackernews_item_get (which fetches a single item) and hackernews_comment_context_get (which gets context around a comment). The phrase 'by id' and the schema's 'comment tree' further clarify the scope.
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 implies usage for listing comments on a story and warns to use cursor when paginated, which is helpful. However, it does not explicitly state when not to use this tool or mention alternatives on the same platform (e.g., hackernews_item_get for retrieving a single comment). The guidance is clear in context but lacks explicit exclusions or comparisons.
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.