humanizer_learn_from_edit

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

With no annotations the description carries the full burden, and it does real work: it discloses the return contents (changed sentences, removed phrases, general rules) and, critically, that save=true persists effects into the pattern checker and rewrite prompt 'from then on'. That is exactly the kind of durable side effect an agent needs warned about. It stops short of clarifying the scope of persistence (per-user vs global) or what the backend parameter controls.

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?

Three short sentences, front-loaded with purpose and followed by returns and then the side effect. Every sentence earns its place and nothing is padded.

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?

For a four-parameter tool with no annotations and no output schema, the description covers purpose, return contents, and the save side effect well. It is incomplete on the backend parameter and on where saved rules and phrases are stored, which matters for a tool with persistent effects.

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, and it does for three of four parameters: ai_text is the AI draft, edited_text is the hand-fixed version, and save triggers persistent rule/phrase registration. The backend parameter is never mentioned in either schema or description, leaving a genuine hole.

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?

States a specific verb and resource - learn the author's taste - and defines the exact inputs that produce it: an AI draft plus the hand-fixed version. That pair-of-texts framing naturally separates it from single-text siblings like humanizer_lint_text, though no sibling is named explicitly.

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?

Usage is implied by the input shape: call it when you have both an AI draft and a human-corrected version. There is no explicit when-not guidance and no mention of the closely related humanizer_compare or humanizer_assess_pair, which an agent could easily confuse with this tool.

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