wordstat.hf.suggest_negative_keywords

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

No annotations are provided, so the description carries the full burden. It reveals that the suggestion is lexicon-based (suggesting deterministic, non-LLM output), which is useful, but says nothing about whether output is purely advisory, return size limits, or latency/cost. For a zero-annotation tool, this is a thin 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?

A single front-loaded sentence with no filler beyond the 'Human-friendly:' group prefix. It is efficient, though the extreme brevity leaves gaps rather than trimming waste.

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?

With no annotations, no output schema, and only one of the three parameters documented, the description should compensate but does not. An agent cannot tell what shape the suggested tokens take or how they are derived from the input phrases.

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 67% (language and max_candidates are documented in the schema; required 'phrases' is not). The description adds no parameter meaning at all — no tokenization behavior, no how-phrases-are-consumed detail, no interaction with max_candidates.

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?

States a specific verb (suggest) and resource (negative keyword tokens) sourced from phrases, and the 'negative' qualifier implicitly separates it from the sibling wordstat.hf.suggest_keywords. It does not explicitly name that sibling, so the differentiation is only implicit.

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?

There is no guidance on when to use this tool versus wordstat.hf.suggest_keywords or direct.hf.set_campaign_negative_keywords, nor any prerequisite or exclusion. The only context is the 'Human-friendly:' group prefix, which conveys nothing about usage conditions.

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