Detect Faces/Objects (YOLO)

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

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose return categories (classes, labels, boxes, crops, scores) and the endpoint, but it does not mention default model behavior, no-detection responses, errors, or confirm that the operation is non-mutating.

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?

The description is a single well-structured sentence that front-loads the action and resource, then gives the endpoint and return values. There is no redundancy or filler.

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 simple two-parameter tool with full schema coverage and no output schema, the description is mostly complete: it states the task, method, endpoint, and return categories. A small gap is lack of guidance on model defaults or how to choose from the possible model values, but the schema partially covers the latter.

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?

The schema already documents both parameters fully, including accepted image formats and the model name source (sdnext_list_detailers). The description adds no parameter-level detail beyond the schema, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Detect'), names the resource ('faces/objects'), identifies the technique (YOLO), gives the exact API path (/sdapi/v1/detect), and lists return categories. This clearly distinguishes it from sibling tools such as sdnext_nudenet, sdnext_tagger, or sdnext_caption.

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 usage context is implied: use this tool when face/object detection on an image is needed. However, it does not explicitly contrast with detection- or analysis-related siblings like sdnext_nudenet, sdnext_tagger, sdnext_vqa, or sdnext_analyze, and gives no when-not-to-use guidance.

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