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

Annotations already cover the safety profile (readOnly, non-destructive, closed-world), and the description adds a meaningful expectation caveat that the output is a backward-looking fit rather than a forecast guarantee. It also enumerates the returned fields, which no structured field provides.

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?

Four compact sentences ordered purpose, routing cues, caveat, returns; nothing is repeated from the title or schema, and the return list compensates for the absent output schema.

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 compensates by enumerating returned fields, and the single parameter is fully documented in-schema. It is nearly complete for a single-input statistical tool; only an explicit pointer to sibling volatility tools is missing.

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% and the sole parameter's minItems constraint plus rationale (at least 50 values needed to identify persistence) is already documented in the schema. The description adds no syntax or preprocessing detail beyond what the schema states, so the baseline 3 applies.

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?

States a specific model (GARCH(1,1) fit by maximum likelihood), names the estimated quantities (omega/alpha/beta) and the output (next-period volatility forecast). This clearly separates it from siblings like run_implied_volatility or run_evt_tail_risk.

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?

Gives concrete user phrasings that should route here and sets a boundary that it is a statistical fit, not a prediction guarantee. It does not name alternative volatility tools (e.g. implied volatility, EVT tail risk) as an explicit when-not branch, so it falls short of a 5.

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