vision_compare

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

No annotations are provided, so the description carries full burden. It mentions returning structured differences with confidence levels, which adds transparency, but lacks details on side effects, prerequisites, or limitations (e.g., supported formats, performance).

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?

Description is concise with bullet points for use cases and ends with a clear statement about return values. Every sentence adds value, and the structure is front-loaded with the core purpose.

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 (5 params, output schema exists), the description covers the main purpose and return format, but parameter guidance is lacking. Overall adequate for a comparison tool, though more detailed parameter descriptions would improve completeness.

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 0% (no parameter descriptions). The description does not explain parameters beyond the schema (names, types, defaults). For a tool with 5 parameters, this is insufficient; the description should at least clarify 'focus', 'prompt', 'include_raw', and 'include_source_ref'.

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 clearly states the tool compares two or more images and identifies differences, listing specific use cases like UI regression testing and design comparison, which distinguishes it from sibling tools like vision_analyze or vision_extract_text.

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 explicit use cases (UI regression, design vs implementation, bug screenshot, version diff) but lacks guidance on when not to use or alternatives; context from sibling tools implies other tools for analysis, but no exclusions are mentioned.

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