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

With no annotations provided, the description fully takes on the burden of disclosure. It reveals that the tool returns a before/after analysis including burstiness, hedge/transition density, abstract-noun ratio, and specific mechanical sentences with triggered signals. It also states it reports failures rather than hiding them, and describes the cost structure. This is exceptionally transparent.

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 moderately long but each sentence contributes essential information: purpose, output details, limitations, cost. It is well-structured and front-loaded with the core purpose. A slight reduction could be made by omitting the cost detail, but it remains efficient for a complex tool.

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 tool's complexity (rewriting with analysis, schema with 5 parameters, no output schema), the description covers nearly all necessary aspects: input requirements, output format (before/after analysis with specific metrics), behavioral traits (declines certain inputs, reports failures), and cost. It is complete enough for an agent to understand what to expect.

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 input schema has 5 parameters with 60% coverage (descriptions for voice, intended_use, reading_level). The description adds value by clarifying that intended_use is 'not the safeguard' and that text is subject to structural checks, not self-certification. It also explains that voice can be a profile from the brand-voice tool. This helps agents understand parameter semantics beyond schema.

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: rewriting text for tone, cadence, and brand voice while preserving meaning. It distinguishes from sibling tools like content_ai-score (which likely scores AI content) and design_ai-slop-detect (which detects AI slop). The verb 'rewrite' is specific and the resource 'text' is obvious.

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 explicitly states that the tool declines academic manuscripts and requests to defeat AI detection, providing clear when-not-to-use guidance. It implies the tool is for everyday content rewriting. However, it does not explicitly mention alternatives among siblings, though the context suggests a distinction from detection-focused tools.

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