humanizer_humanize_text

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 disclose that rewriting happens server-side via a model backend and that some flags are 'slower'. It does not say whether the original text is preserved, whether anything is persisted, what model costs or latency to expect, or what the response contains, leaving meaningful behavioral gaps for a mutating-in-spirit pipeline.

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?

A single front-loaded purpose sentence followed by one line per argument is efficient and scannable, with no filler prose. The arg block is long but justified because the schema documents nothing itself.

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 nine-parameter tool with no annotations and no output schema, the definition covers inputs but not what comes back: the rewritten text, lint results, and fact-check report are described only as actions, not as returned structure. It also omits whether output is saved versus merely returned, a real ambiguity given the humanizer_save_document sibling.

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 arg list is the only source of parameter meaning, and it supplies value sets (level: light/standard/deep; backend: auto/claude-cli/anthropic/mock) plus effect hints like 'slower' and default 'auto'. It is undercut by an internal inconsistency: local_model says it applies when backend is 'ollama or openai-compatible', values absent from the backend list, which could cause a failed call.

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 opening sentence names a specific verb and resource (rewrite text) and discloses the pipeline steps that follow (lint, fact-check), which distinguishes it from pure-assessment siblings. It stops short of explicitly contrasting itself with humanizer_humanize_file or humanizer_assess_text, so the sibling boundary must still be inferred.

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 only implied: the arg notes hint that 'assess' or 'semantic' should be turned on for extra scrutiny and that 'voice' relates to humanizer_voice_profile, which is a useful pointer to a sibling. There is no explicit statement of when to prefer this tool over humanizer_humanize_file, humanizer_lint_text, or humanizer_assess_text, and no prerequisites or exclusions are given.

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