compare_images

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

With no annotations, the description carries the full burden. It discloses that the model compares images directly across images (not separately analyzing then stitching) and details the returned information, including visual hierarchy changes. It doesn't mention limitations or failure modes, but core behavior is well covered.

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 moderately sized but front-loaded with the main action, followed by return details, use cases, and parameters. Every sentence adds value, though it could be slightly more concise by merging repetitive parameter mentions.

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 multi-image comparison tool with no output schema, the description adequately covers purpose, timing, parameters, and return categories. It lacks explicit output formatting or failure details, but is sufficient for an agent to select and invoke the tool correctly.

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 covers 100% of parameters, including image_paths order semantics, path formats, and optional prompt. The description reiterates order semantics but adds no new information beyond the schema, so the baseline 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?

The description clearly states the tool compares 2-8 local images using a vision model, with a specific list of output categories (summary, differences, layout changes, etc.). It distinguishes itself from sibling tools like analyze_image and inspect_ui by emphasizing cross-image comparison rather than single-image analysis.

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 provides explicit invocation timing under '主动调用时机', listing scenarios such as design-to-implementation comparison, visual regression, and multi-scheme evaluation. However, it does not explicitly name sibling tools as alternatives or state when not to use it, so it lacks full exclusion guidance.

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