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

No annotations are provided, so the description carries the full burden, and it delivers: it discloses async/cold-start delay, the 'processing' status, the 25-word/400-word boundaries, confidence semantics ('NOT an AI probability'), and the probabilistic, non-authoritative nature of results. There is no contradiction with annotations because there are none.

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?

Every sentence earns its place and follows a sensible order: purpose, input constraints, output semantics, performance warning, continuation, and caveat. It is long but information-dense, with no filler or repeated schema content.

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?

The description is unusually complete for a no-output-schema, no-annotation tool: it defines return fields, async behavior, and text limits. The remaining gap is optional parameter semantics (threshold, zero_retention_mode, exact max_wait behavior), which keeps it from being fully complete.

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; it explains the text parameter thoroughly (minimum length, chunking, concatenation) and indirectly references max_wait_seconds via 'wait budget.' However, it gives no semantics for threshold or zero_retention_mode, which remain cryptic without schema descriptions.

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?

Description opens with 'Detect whether TEXT ... was written by an AI language model,' a specific verb, resource, and input type. It also distinguishes this tool from siblings by focusing on text and explicitly naming get_text_detection for resuming an async run.

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?

Explicitly lays out when the tool is usable: text must be at least 25 words, shorter inputs should be concatenated, longer documents should be chunked, and queued runs can be resumed with get_text_detection(uuid). This provides actionable when-to-use and alternative routing, not just a vague hint.

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