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

Annotations already declare readOnly/idempotent/non-destructive, so safety is covered. The description adds real behavioral context beyond that: the three detection rules, the 7-day cluster window, the 5-trading-day announcement cross-reference, and the note that thin NZX liquidity makes it especially sensitive. It does not describe pagination or limits, keeping it at 4.

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?

Purpose is front-loaded in the first sentence, followed by methodology, then usage triggers. It is dense and every sentence contributes, though the anomaly-type enumeration and market-context sentences make it longer than strictly necessary.

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 an output schema present, return values need not be re-explained, and annotations cover the safety profile. Given the 100% parameter coverage, the description supplies the remaining needs: detection methodology, cross-referencing behavior, and when to invoke it. Nothing material is missing.

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 days, ticker, and min_zscore are fully documented (including defaults and max). The description explains the threshold concept ('z-score > threshold') but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb (detect) and resource (volume anomalies) plus the exact methodology (z-score on daily OHLCV) and the three anomaly subtypes it returns. This clearly differentiates it from generic siblings like get_anomalies or get_technical_signals without needing to open a schema.

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?

The final sentence gives explicit user-intent triggers ('unusual volume, trading activity, accumulation, distribution, or pre-announcement patterns'), which is strong usage guidance. It stops short of naming alternatives or when-not-to-use conditions against similar siblings such as get_anomalies or get_market_signals.

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