hackernews_comment_context_getGet a Hacker News comment with its ancestor chain to the root story. Accepts a comment id.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Hacker News comment id. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds value by specifying the behavioral trait of returning the ancestor chain to the root story, which is not in the annotations. However, it does not disclose potential edge cases (e.g., invalid id, non-comment id) or the exact return structure, so it falls short of rich disclosure.
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 sentences, zero fluff. The first sentence front-loads the core action and result; the second clarifies the input. Every word earns its place, and the structure is optimal for quick comprehension.
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 read-only tool with one parameter and no output schema, the description adequately conveys what is returned (comment plus ancestor chain). It does not mention error handling or output format, but those are minor gaps given the tool's low complexity. Overall, it is sufficiently complete for an agent to invoke it correctly.
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 schema description for the single parameter id already states 'Hacker News comment id' with 100% coverage. The description's phrase 'Accepts a comment id' simply reiterates the schema without adding additional semantics. Since schema coverage is high, the baseline is 3, and no extra meaning is provided.
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 specific verb and resource: it gets a Hacker News comment including its ancestor chain to the root story. This clearly differentiates it from generic item retrieval (hackernews_item_get) or comment listing (hackernews_story_comments_list) and leaves no ambiguity about the tool's function.
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 when to use this tool: when you need the full ancestor chain of a comment, not just the comment itself. However, it does not explicitly exclude alternatives like hackernews_item_get for single-item retrieval, so it lacks explicit 'when-not' guidance. Still, the context is clear and sufficient for an agent to infer the appropriate use case.
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.