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

With no annotations, the description carries the full burden and does well: it declares determinism, no LLM involvement, explainability, the exact shape of returned patterns, and that no rewrite occurs. It omits operational traits an agent would want, such as whether the call consumes credits (a buy_credits sibling exists) or any rate/latency characteristics.

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?

Dense but front-loaded: purpose, return shape, pattern taxonomy, and the rewrite boundary all land in a few tight sentences. A slight dip into marketing register ('Explainable, not an opaque classifier') is redundant with 'every point is a named, located pattern', which says the same thing more concretely.

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 describe the return value, and it does so completely: score range, band values, fired patterns with sample spans. Combined with the schema-documented input, an agent has everything needed to call and interpret the result.

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% and there is only one parameter, whose 100k-char limit is already documented in the schema. The description adds nothing about the text parameter beyond the implied 'POST prose' framing, so the baseline of 3 applies.

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?

Specific verb and resource ('Detect AI-writing tells in text') followed immediately by a concrete explanation of what comes back: a 0-1 ai_ness score, a band, and the exact named patterns with sample spans. The closing line 'The rewrite is a separate step' distinguishes it from the sibling humanize tool without the agent needing to open either schema.

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 makes the division of labour clear: this tool scores, the rewrite is a separate step, which effectively routes the agent to humanize for rewriting. It also frames the tool's niche ('explainable, not an opaque classifier'), which tells the agent why to pick it. However, it never explicitly states when NOT to use it or names the sibling directly.

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