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

With no annotations, the description carries the full burden of behavioral disclosure, yet it never says whether the operation is read-only, requires authentication, or has side effects. It does not describe what the audit returns (pass/fail, scores, flagged violations) or how thresholds are applied. The only behavioral content is the list of problems it checks for, which is insufficient for a 5-parameter, no-output-schema tool.

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 English sentence is reasonably front-loaded, but the same content is repeated in Spanish and Chinese, roughly tripling length without adding information for an agent that reads English. The parenthetical marketing labels ('Proof-of-Inference & Anti-Model-Downgrade') consume space without operational value. Signal-to-noise is poor relative to the amount of actual guidance delivered.

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?

For a tool with five parameters, no annotations, no output schema, and zero schema description coverage, the description should explain parameter usage and expected results. It omits return-value behavior entirely and leaves threshold semantics undefined, so an agent could not confidently construct a correct call from this text alone.

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 0%, so the description must compensate. It does so partially by conceptually mapping to parameters: LLM output (responseText), token billing inflation (billedOutputTokens), repetitive collapse (minUniqueWordRatio), and constraint violations (forbiddenTerms / mustIncludeTerms). However, it never states units, expected formats, whether thresholds are ratios or counts, or how the term lists are matched, leaving real gaps.

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 specific verb (audit) against a specific resource (LLM output) with named failure modes: token billing inflation, quantization collapse, and constraint violations. This clearly separates it from sibling audit tools such as audit_api_sla_escrow or audit_quant_backtest_escrow. It loses a point because the jargon ('Vertical 7', 'Anti-Model-Downgrade') is not defined and the practical output of an 'audit' is left implicit.

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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. The only contextual hint is the 'Vertical 7' label, which assumes the agent already knows the vertical taxonomy. Nothing tells the agent under what conditions this audit should be invoked versus the other audit_* siblings.

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