Check text for AI-writing style tells

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

With no annotations, the description fully explains behavior: it is deterministic, local (no LLM call, no network), and always returns the same number for the same input. It also outlines the output structure: score, verdict, per-dimension sub-scores with raw counts, and up to 6 concrete fixes. This exceeds the burden typically required without annotations.

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 three sentences, each adding essential information: purpose and measurement components, output details, and deterministic/local nature. No word is wasted, and critical caveats are included up front.

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?

Given the simple input schema and lack of output schema, the description covers all necessary context: what input is expected, how to interpret results, and behavioral guarantees. It also addresses edge cases (short text) and limitations (style vs. authorship), making it sufficient for an agent to use 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 sole parameter 'text' is described with a helpful note about reliability at ~200 characters and behavior for shorter text. Since schema coverage is 100%, the description adds value beyond the schema by providing contextual usage hints, earning a 4.

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 tool's purpose: scoring prose on a human-likeness scale from 0 to 100. It specifies exactly which style tells are counted (LLM word/phrase density, em-dash density, formulaic structures, etc.), distinguishing it from siblings like 'grade_landing_copy' which likely targets landing pages.

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 provides usage guidance by noting the minimum character count for reliable results and that shorter text still works but skips a dimension. It also warns that this measures style, not authorship, so it's not an AI detector. However, it does not explicitly compare to sibling tools or state when not to use them.

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