hn_signal

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It does disclose the core behavior (returns a story count aggregated over N days), but omits details like query matching semantics, day boundaries, and whether the result is a raw number or an object.

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?

One compact sentence with no filler; the core behavior and intent are front-loaded and the parenthetical adds useful context without bloating the text.

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 low-complexity tool with no output schema and no annotations, the description gives the essential what and why, but not enough to fully preempt ambiguity about return shape, parameter constraints, or relation to hn_trend. It is minimally viable rather than comprehensive.

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 coverage is 0%, so the description must compensate. It maps 'query' to the search term and 'days' to 'last N days', which adds meaning beyond the raw schema. However, it leaves out query syntax, default/range for days, and whether days is inclusive.

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 operation: count Hacker News stories for a query over a time window, and adds the 'tech-audience attention proxy' framing. It does not explicitly distinguish itself from the sibling hn_trend, but the verb/resource/scope are clear.

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 conveys a use case (query-level HN story count as attention signal) but gives no explicit when-to-use or when-not-to-use guidance. With hn_trend as a sibling, the agent must infer which tool is appropriate rather than being told.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.