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

Annotations already declare readOnlyHint and openWorldHint, and the description adds meaningful behavioral context: it uses two timeframes, derives values from native klines, and returns both annualized and per-period percentages. It does not detail lookback windows or rate limits, but the safety profile is already covered by 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 concise and front-loaded: three short sentences convey the metric, timeframe, data source, output format, rationale, and usage context without repetition or filler.

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 one-parameter, read-only tool with strong annotations, the description is complete: it explains calculation, timeframes, source, returned values, and intended use. No output schema exists, but the output composition is sufficiently specified.

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?

The schema fully documents the single `symbol` parameter with pattern, length, and an example. The description adds no additional parameter-level detail, so the baseline of 3 applies given high schema coverage.

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 identifies the tool as computing realized volatility from close-to-close log returns across two timeframes, sourced directly from Binance native klines. This distinguishes it from sibling kline/ticker/metric tools by naming a specific derived metric and its calculation basis.

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 an explicit use case: cross-checking grid width calibration for the i_atrMult Grid Advisor. It does not mention when not to use the tool or name alternative tools, but the context is clear enough for a simple read-only metric lookup.

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