audit_queue

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

No annotations provided, but the description discloses throttling behavior (~200ms delay per post, extra delay every 10 posts) and mentions it returns a violation report. However, it does not describe the return format or the nature of the violation report, which would be helpful.

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?

Three sentences with front-loaded purpose, immediate usage guidelines, and behavioral detail. No redundant information; each sentence serves a distinct purpose.

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?

The description covers key workflow and throttling, but lacks details on the violation report's structure or content. Without an output schema, this gap could hinder an agent's ability to present results to the user effectively.

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 has 0% description coverage, so the description must add meaning. It explains dry_run and post_comments with usage guidelines, but does not clarify the queue or sprint parameters beyond their names. Partial value added but not fully compensating for the schema gap.

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 validates all open issues in a queue against convention.json and returns a violation report. It uses a specific verb-resource combination and distinguishes from sibling tools like validate_issue (single issue) and list_queue_sprints.

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 instructs to run with dry_run=true first and only set post_comments=true after user confirmation. This provides clear when-to-use guidance and a safe workflow, effectively differentiating usage of the boolean parameters.

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