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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered structurally. The description adds meaningful behavioral context beyond those annotations by specifying the exact returned fields, the per-day granularity, and the payload-size tradeoff compared to full chain data. This is useful and non-contradictory.

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 three sentences with no filler: the first states the core operation and outputs, the second gives concrete use cases, and the third explains the payload tradeoff. Every sentence earns its place, and the most identifying information is front-loaded.

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 there is no output schema, the description adequately communicates what the tool returns and why it is useful. It covers core fields, granularity, and use context. It could be slightly more explicit about how the optional date range affects the returned summary and what 'year-high/year-low markers' mean, but the schema covers parameter bounds and the domain terms are reasonably self-explanatory.

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 input schema has 100% description coverage: ticker is described as 'Stock ticker (uppercase)' and start_date/end_date are described as optional YYYY-MM-DD bounds. The description does not need to repeat these. It adds mild context by framing the result as per-day summary, but it does not materially extend parameter semantics, so the baseline score of 3 is appropriate.

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 uses a specific verb ('Get') and resource ('per-day implied volatility and historical volatility summary for a ticker') and names concrete return fields (iv_current, hv_current, year-high/year-low markers). It is clear on its own, and the 'smaller payload than full chain data' note helps separate it from chain-heavy tools, but it does not explicitly name or contrast a sibling tool, so it stops short of full sibling differentiation.

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 gives clear use cases: 'IV rank/percentile signals, vol regime detection, and time-series feature engineering.' It also implies when not to use it by noting the payload is much smaller than full chain data. However, it does not explicitly name alternatives or state exclusion conditions, so the guidance is contextual rather than fully directive.

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