audit_corpus

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

The description adds a valuable non-obvious behavior: 'Missing observations remain null, never implicit zero.' This goes beyond the annotations, which only declare the operation as read-only, idempotent, and non-destructive. It informs the agent about data representation, which is critical for interpreting results. The description does not contradict 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 a single, tightly worded sentence that states the core purpose and a key behavioral caveat. No filler or redundancy. The most important information (purpose and null handling) is front-loaded. It earns its place entirely.

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 presence of an output schema, the description does not need to explain return values. It covers the primary action and a critical data-representation detail. The only missing piece is explicit usage context, but that is already scored low under usage guidelines. For a read-only tool with strong annotations and schema coverage, the description is reasonably complete.

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 description coverage is 100%, so all three parameters (corpus, audit_id, include_published) are already documented in the schema. The description does not add parameter-level details beyond what the schema provides. It mentions 'dated observed quality metrics' but that relates to the output, not the parameters. Baseline 3 applies because the schema carries the full load.

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 states a clear verb-resource pairing: 'return dated observed quality metrics'. It distinguishes from published figures, which hints at differentiation from sibling tools like corpus_stats or compare_corpora, though it does not name them explicitly. This is clear but not as sharply differentiated as the calibration example.

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?

No guidance is given on when to use this tool versus alternatives. It does not mention context, exclusions, or a preferred use case. The mention of 'separately from published figures' implies a distinction but does not state when to choose this over corpus_stats or compare_corpora. An agent would infer usage from the name and parameters rather than from explicit guidance.

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