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

No annotations are provided, so the description carries the disclosure burden. It reveals the model restriction (GLM/DeepSeek only) and implies a text output. However, it does not detail error handling, return format, or other behavioral traits like rate limits or auth requirements.

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 efficient with one core sentence and a necessary prefix. It is front-loaded and contains no wasted words. The Chinese prefix could be confusing for non-Chinese agents but is essential for the restriction.

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?

Given the tool's complexity (6 parameters, nested object schema, no output schema, no annotations), the description is too brief. It lacks details on output format, error cases, and process expectations. The sibling tools are not cross-referenced.

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 coverage is 67% (medium) and the description adds no extra meaning to parameters. It does not explain parameter usage or constraints beyond what is in the schema. For a tool with multiple parameters including nested objects, this is insufficient.

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 clearly states the tool's action: answering a specific question using visual evidence from one image. It uses a specific verb and resource, and distinguishes itself from sibling tools like compare_images (two images), describe_image (general description), and ocr_image (text extraction).

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 description implies when to use this tool (for answering specific questions about a single image) vs. siblings. The Chinese prefix explicitly restricts usage to GLM/DeepSeek models. However, it does not explicitly state when not to use it or provide alternatives.

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