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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context by explaining the need for a baseline and suspect set, and enumerates the types of anomalies detected. No contradiction with 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 compact and front-loaded, with the core purpose in the first sentence. The example is useful and not repetitive, and every sentence contributes to understanding the tool's use.

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 rich annotations, complete schema coverage, and a clear example, the tool is well-described for selection and invocation. It does not explain what the output looks like (no output schema), but this is not essential for invoking the tool correctly.

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 the description does not need to add much. It reinforces that 'training' is known-legitimate and 'test' is suspect, but this is already stated in the schema. The sensitivity parameter is not elaborated beyond its default, so the description adds no new semantic value.

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 opens with a specific verb and resource: 'Detect wash trading and fake volume in OHLCV candle data.' It further specifies detection targets (artificial volume spikes, regular patterns, price-volume manipulation), clearly differentiating from sibling tools like waveguard_price_manipulation by focusing on volume authenticity.

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 description provides clear usage context: send known-legitimate candles as training and suspect candles as test. The example ('Send 100 candles from a liquid pair as baseline, test candles from a suspicious pair') makes the intended workflow concrete, though it does not explicitly mention alternatives or exclusions.

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