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

Annotations already mark this readOnly and not open-world, so the bar is lower, yet the description adds real behavioral context: the corpus is measured rather than opinion-based, and 'Pass whichever metrics you have' clarifies that partial input is valid — important since all 7 params are optional with 0/-1 defaults. It stops short of stating what happens with default/omitted values.

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?

Front-loads the core purpose, then the use case, then interpretation guidance — a logical order with no filler sentences. The inline quoted examples are illustrative rather than redundant, though the description runs slightly long.

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?

There is no output schema, so the description correctly takes on return-value explanation (percentile, corpus median/p25/p75, verdict) and passes the interpretation thresholds. It is nearly complete for a 7-parameter tool, with the remaining gap being the meaning of undocumented parameters and their default sentinels.

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%, so the description must carry the load. It explains the return semantics per metric (percentile, median/p25/p75, verdict) and implies px/ms units, which helps, but it never defines base_unit_px, section_rhythm_px, hero_size_px or dominant_duration_ms, nor what the sentinel defaults 0/-1 mean. Only body size and radius are exemplified.

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?

States a specific verb (grade/validate) applied to a specific resource (your design values) against a clearly characterized corpus (measured real products, decoded live). It is distinguishable from siblings like get_web_benchmark or compare_components, which benchmark products rather than validate the caller's own numbers.

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?

Explicitly says when to use it ('anti-slop-check your own output BEFORE shipping') and what counts as normal (p25-p75) vs. needing justification (sub-p10/over-p90). It gives no named alternative tool for a different need, but the temporal guidance is clear and actionable.

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