Get Hacker News item
hackernews_item_getGet a Hacker News item by id.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Hacker News item id. |
hackernews_item_getGet a Hacker News item by id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Hacker News item id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations readOnlyHint=true and openWorldHint=true already communicate safety and openness. The description adds nothing about return format, error conditions, or what constitutes an 'item'. It merely restates the function without enriching the behavioral context beyond what annotations provide.
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, front-loaded sentence with no filler. It communicates the core action and input immediately, 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?
For a simple, one-parameter read-only tool with full schema coverage and annotations, the description is nearly sufficient. The only gap is that it does not clarify what kind of item is returned (story, comment, etc.), but given the low complexity, this is a minor omission.
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% for the single id parameter, which is described as 'Hacker News item id'. The tool description adds no further meaning, but since the schema already documents the parameter adequately, the baseline of 3 applies.
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 ('Get') and the resource ('a Hacker News item by id'). It is unambiguous but does not differentiate from sibling tools like hackernews_story_get or hackernews_comment_context_get, which also fetch HN content. The verb and resource are specific enough for basic identification.
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 provided on when to use this tool versus the many hackernews_* siblings. There is no mention of whether this returns stories, comments, or other items, nor any hint about when to prefer it over hackernews_story_get or hackernews_maxitem_get. The agent must 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.