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

With no annotations, the description carries the full burden and does substantial work: it discloses that deep is slower and quota-limited, that unreadable languages yield no score with a stated reason, and that scores are estimates that can err in both directions. It omits permission/auth requirements, but failure modes and cost tradeoffs are well covered.

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 core capability and output shape are front-loaded in the first sentence, followed by parameter guidance and caveats. It is somewhat dense and the closing ethical sentences are more advisory than operational, but nothing is redundant.

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?

There is no output schema, so the description must explain return values — and it does: numeric score, band, named reasons with supporting quotes, plus the no-score case. Combined with the deep/lang guidance, an agent has everything needed to call it 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?

Schema coverage is 100%, so baseline is 3, but the description adds real meaning: deep is not just 'more accurate' but slower and quota-limited, and the reasons are written in the lang language. That context about the cost/benefit of deep goes beyond the schema's terse wording.

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 specific verb and resource — score text 0-100 for machine-writing signs — and specifies the exact output shape (score, band, named reasons with quoted evidence). It is clearly distinguishable from siblings like check_ai_image and scan_url, which handle images and URLs rather than raw text.

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?

It gives explicit conditional guidance: set deep=true for the most thorough read, and unsupported languages return no score with an explanation. It does not, however, route the agent to alternatives (e.g. scan_url for a URL, cleanup_text for rewriting), so sibling selection is left to inference.

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