whodb_audit

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

Annotations already indicate readOnly and idempotent, so safety is clear. The description adds behavioral details like the specific checks performed (null-rate spikes, orphaned foreign keys) and that it returns audit results per table, going beyond annotations.

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 well-structured: a clear single-line purpose, bullet points for usage, a JSON example, and return summary. Every sentence is informative and front-loaded with essential information.

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 tool's complexity, the description covers scope, specific checks, example usage, and return value summary. Output schema exists, so detailed return documentation is not needed. The description is sufficient for effective tool selection and invocation.

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 descriptions already cover all 5 parameters (100% coverage). The description adds a usage example and practical context (e.g., null_warning/error thresholds) but does not significantly enhance meaning beyond the schema.

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 'Run data-quality checks on one schema or table' and lists specific checks like null-rate spikes, missing primary keys, etc. This distinguishes it from sibling tools like whodb_query or whodb_columns, which have different purposes.

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?

Explicit 'Best for' and 'Not recommended for' sections provide clear when-to-use guidance. 'Common mistakes' offers practical warnings to prevent misuse, e.g., forgetting to scope to one table.

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