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

Annotations declare readOnlyHint=true, so the safety profile is covered. The description adds useful behavioral context beyond that: supported input formats, JPEG output, and per-call cost. It does not mention edge cases such as when no face is found, but that is a minor gap for a read-only transformation.

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 front-loaded with the core action, and the Korean sentences add non-redundant details such as JPEG return format, supported image formats, and cost. There is no filler or unnecessary repetition.

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 read-only tool, the description covers the essential operation, input formats, and return format. Minor omissions are an explicit alternative to face_detection and behavior when no face is detected, but with full schema coverage this is nearly 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 coverage is 100%, and the schema already documents image_url requirements, allowed MIME types, size limit, threshold range, default, and semantics. The description adds no parameter-specific meaning beyond the schema, so the baseline 3 applies.

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 description names the exact operation ('detect faces') and result ('blur/mosaic them') plus the JPEG output format, which clearly distinguishes it from sibling face_detection and other image tools. The Korean sentence reinforces the resource and output without ambiguity.

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?

No guidance is given about when to choose this tool over alternatives such as face_detection or image_edit. The privacy-blurring use case is implied but never stated as a selection criterion or contrasted with sibling tools.

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