stats_realized-volatility

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

Annotations already indicate readOnlyHint=true and idempotentHint=true, so the tool is safe and deterministic. The description adds behavioral context by listing the outputs: realized volatility, annualized vol, and rolling vol series. No contradictions 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 brief with only two sentences: the first lists the volatility methods, and the second states usage and outputs. Every sentence is essential, no redundancy.

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 complexity (multiple methods) and no output schema, the description is adequate but has a minor inconsistency: it says 'Provide returns' but the input schema requires price arrays (close, low, high, open). This could confuse an agent. It could also clarify which parameters are needed for each method.

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 baseline is 3. The description mentions 'Provide returns and optional annualization factor' but does not add significant meaning beyond what the parameter descriptions already provide. It adds minimal 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 clearly states it computes realized volatility using multiple methods (close-to-close, Parkinson, Garman-Klass, Yang-Zhang) from OHLC data. This distinguishes it from sibling tools like stats_sharpe_ratio or stats_zscore which compute different metrics.

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 explicitly says 'Use when computing historical/realized volatility from a return series.' It provides clear context but does not mention when not to use it or explicitly name alternatives. However, the context is sufficient for selection.

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