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

Annotations already convey read-only, open-world, and non-destructive behavior; the description adds valuable behavioral context above that: the same fetch, 15-minute cache, quota limits, absence of a quota line, and deliberate stripping of scoring internals while retaining evidence-class tags. These details materially affect how an agent interprets results and manages calls.

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 long but every sentence is informative and the core purpose is front-loaded in the opening sentence. The first paragraph is a dense single-sentence enumeration of fields that could be more scannable, but it is structured into two logical sections and achieves a good signal-to-noise ratio.

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?

With no output schema, the description fully carries the burden of explaining the JSON response shape, and it does so thoroughly: top-level fields, layers/checks structure, status enum, evidence-class tags, and the explicit absence of ranking and scoring internals. Caching, quota behavior, and differences from lekta_audit are also covered. Nothing important for correct invocation and interpretation is missing.

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 input schema already provides a thorough description of the url parameter (absolute http(s), scheme defaulting, fragment dropping, hostname lower-casing, query significance, one page per call, and cache behavior). Schema description coverage is 100%, and the tool description adds no additional parameter-level meaning, 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?

States a specific verb ('runs the same page audit') and output resource ('whole report as one JSON document'), and explicitly contrasts it with the Markdown output of lekta_audit. The phrase 'for programs, not people' clarifies the intended consumer. This fully distinguishes it from its most similar sibling.

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 call lekta_audit instead (when the quota count is needed) and notes that the JSON carries no quota line. It also warns that nothing is ranked and that checks arrive in layer order, helping an agent decide whether this format fits the use case. While it doesn't discuss lekta_diff or lekta_fix_plan, those differ in tool purpose and are not confusable.

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