visual_check

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

With no annotations, the description discloses key behaviors: set replaces baselines, check returns a verdict with diff image, default threshold (0.5%), and storage per project+name. It does not detail error handling or authentication, but covers the core workflow well.

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 a single, information-dense paragraph that covers purpose, actions, defaults, and best practices without unnecessary words. It is well-structured for quick reading, though splitting into bullet points could improve scanability.

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 complexity (7 parameters, no output schema), the description lacks details on the return format for 'check' and error cases (e.g., missing baseline). It covers the main workflow but could be more complete for a tool without structured output documentation.

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 description adds value by explaining the role of each action, default values (e.g., threshold, max_diff_percent), and the recommendation for selector usage. This goes beyond the schema's parameter descriptions.

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 purpose: managing named visual-regression baselines with two actions (set and check). It distinguishes itself from sibling tools like compare_screenshots by focusing on baseline management rather than one-off comparisons.

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 practical guidance: re-baseline on intended changes, and prefer selector-scoped over full-page baselines to avoid flakiness. It does not explicitly list when to use alternative tools, but the advice is sufficient for the primary use case.

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