argus_visual_diff

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

No annotations exist, but the description details behavioral traits: first call saves baseline, subsequent calls compare, returns different signals with thresholds (0.1% warning, 5% critical), baseline storage path, and Chrome prerequisite. It covers the main behaviors.

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?

Well-structured: starts with core action, then first/subsequent usage, then CI context, then parameter guidance, then prerequisite. Slightly long but every sentence adds value and is logically ordered.

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?

No output schema, but description explains return values (visual_baseline_created, visual_regression, visual_diff_summary). Covers prerequisites (Chrome on port 9222) and threshold details. Complete for the tool's complexity.

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% (all 3 params described in schema). The description adds practical context: explains updateBaseline use for intentional UI changes, url reachability requirement, and baselineDir override. Adds value beyond schema.

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 core purpose: screenshot baseline comparison via pixelmatch. It distinguishes from sibling tools like argus_audit or argus_compare by focusing on visual regression detection between screenshots.

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?

Provides explicit context for use: CI or fix loops, and explains first-call vs. subsequent-call behavior. Lacks an explicit 'when not to use' but contrasts with 'full audit' and gives clear lifecycle.

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