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

Annotations include readOnlyHint=true, and the description adds useful context about return contents (fitted parameters, persistence, half-life, multi-horizon forecast) and default distribution. No contradiction exists; it responsibly complements the read-only annotation.

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 information-dense yet compact, using three focused sentences. Each element (models, returns, usage, dist option) earns its place without 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?

With no output schema, the description sufficiently explains return values (fitted params, persistence, long-run vol, half-life, multi-horizon forecast) and provides model context. It covers the essential aspects a caller needs to know for a complex forecasting tool.

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 coverage is 100% with clear parameter descriptions. The description reinforces the dist default (student_t) but adds little beyond the schema for symbol or apiKey. Baseline 3 is appropriate when schema carries the parameter detail.

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 the tool produces conditional volatility forecasts using specific models (EWMA, HAR-RV, GARCH(1,1)) and lists the outputs. This distinguishes it from sibling volatility tools by emphasizing 'conditional' and model specificity.

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?

It explicitly says 'Use to forecast next-day/forward volatility or gauge vol mean-reversion,' providing clear when-to-use guidance. However, it does not mention when not to use it or name alternative tools, so it stops short of full guidance.

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