Anime Tagger

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

No annotations are providedaine, so the description must carry transparency weight. It reveals the underlying model and API endpoint, but doesn't mention computational cost, possible GPU load, rate limits, or that it performs no state changes. For a compute-heavy operation with 11 parameters, this is a noticeable gap in behavioral disclosure.

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?

Two concise sentences; purpose, endpoint, and model reference are clearly front-loaded with no wasted words.

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?

With 11 parameters and no output schema, the description should guide on when to adjust thresholds, how tags are returned (scores vs names), and interplay with models. It covers the endpoint and model list pointer but lacks output format and usage guidance for parameter tuning. Falls short of what a full-featured tagging tool needs.

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 64% (6/11 + default model mentioned in description). The description adds context about the tagger endpoint and model family but doesn't explain undocumented parameters (sort_alpha, use_spaces, show_scores, include_rating). The reference to sdnext_list_tagger_models helps selection. Baseline 3 acceptable.

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?

Clearly states it tags images with Danbooru-style tags using wd-tagger / DeepDanbooru, and names the endpoint (sdapi/v1/tagger). The verb 'tag' plus resource 'image' and sibling tool list (e.g., sdnext_list_tagger_models) distinguish it from other tools.

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?

Description provides the purpose and cross-references sdnext_list_tagger_models for model selection, but does not explicitly state when to use this tool versus alternatives (e.g., captioning tools like sdnext_caption). No exclusions or prerequisites beyond image input.

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