hackernews_user_favorites_listList favorites for a Hacker News user by username. Returns a list (use page when paginated; 1-based).
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | One-based HTML favorites page number. Default: 1. Each page bills 1 credit. | |
| username | Yes | Hacker News username (case-sensitive). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the read-only nature is disclosed. The description adds the pagination detail: 'use page when paginated; 1-based,' which is useful and not redundant. However, it does not elaborate on any rate limits, error conditions, or the structure of the returned list beyond saying 'Returns a list.' Given the annotations cover the main behavioral trait, 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?
The description is a single sentence that packs the essential information: what the tool does and a critical pagination note. There is no fluff or repetition. It is front-loaded with the purpose and keeps the pagination caveat in a parenthetical, making it easy to scan. 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?
The tool is a simple read-only list operation. Annotations already convey openness and read-only safety. The schema fully defines parameters, including defaults and billing notes. The description covers pagination and return type. Given the lack of an output schema, the description could optionally detail the list item fields, but for a typical favorites list, the absence is not a critical gap. Overall, it is sufficiently complete 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?
The input schema already provides full descriptions for both parameters: username is 'Hacker News username (case-sensitive)' and page is 'One-based HTML favorites page number. Default: 1. Each page bills 1 credit.' With 100% schema coverage, the description adds no extra semantic value beyond what the schema states. It only rephrases the pagination hint. Thus, the baseline of 3 is justified.
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 tool 'List favorites for a Hacker News user by username.' It specifies the action (list), the resource (user favorites), and the parameter (username). This distinguishes it from sibling Hacker News tools like hackernews_user_comments_list and hackernews_user_submissions_list, which target different content types. The purpose is 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?
The description provides no explicit guidance on when to use this tool versus alternatives such as hackernews_user_comments_list or hackernews_user_submissions_list. It does not mention exclusions, prerequisites, or specific scenarios. The only hint is the name and the mention of 'favorites,' but the description itself offers no decision support for an agent choosing among the many user-content list tools.
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.