humanizer_lint_text

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

With no annotations, the description carries the full disclosure burden and mostly succeeds: it explains the scoring direction (0-100, higher = more AI-like), the grouping of results, and an important behavioral quirk (quotations are skipped except chatbot artefacts). It does not state whether the check is read-only/side-effect free or how large inputs are handled, leaving minor gaps.

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 tightly packed sentences with no filler; the core action is front-loaded, followed by the return shape and the exception rule in one clause. Every sentence 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?

With no output schema, the description usefully enumerates the return payload (score, category counts, per-finding line/text/message/fix), so an agent knows what to expect. It falls just short because the max_findings parameter's role is unaddressed.

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 0%, so the description must compensate. 'text' is self-evident and the mention of findings implicitly relates to output, but max_findings (default 100) is never explained — an agent cannot tell from the definition that it caps the number of findings returned or what happens when exceeded.

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 and resource ('Check text for AI-writing tells') and adds a distinguishing trait ('without using a model') that separates it from model-based siblings like humanizer_assess_text. It stops short of naming that sibling directly, so the differentiation is implicit rather than explicit.

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 phrase 'without using a model' implies a deterministic, rule-based pass, which hints at when to prefer this over model-backed assessment. However, there is no explicit when-to-use/when-not-to-use guidance and no named alternative among the siblings (humanizer_assess_text, humanizer_humanize_text, humanizer_compare).

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