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

The annotations already establish read-only and closed-world behavior. The description adds useful behavioral context — baseline comparison, fixed counts, warning budget, and burndown — but it never explains the baseline lifecycle or where the baseline is passed in.

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?

It is a single front-loaded sentence focused on the baseline workflow, with little wasted text. The 'PRO —' prefix and dense parenthetical feature list add some marketing noise but do not obscure the main idea.

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 should explain what is returned and how the baseline is reused. Instead it leaves the call/return lifecycle ambiguous and references a baseline handoff that the input schema does not support.

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%, so document and context are self-documenting. However, the description implies a 'baseline' value must be passed back even though no baseline parameter exists in the schema, creating a misleading parameter expectation.

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?

It states a specific outcome — snapshot existing violations as a baseline and fail only on new ones — so it is more than a restatement of the name. However, it describes two phases ('snapshot' then 'pass baseline back') without clarifying which phase this call performs, and it does not differentiate itself by naming a sibling tool.

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?

It gives a clear usage scenario: adopting a ruleset on a legacy API with existing violations, to avoid a wall of red and fail only on new violations. It does not explain when to use validate_api, validate_ruleset, or other validation siblings instead.

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