stats_garch-forecast

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

Annotations already state readOnlyHint=true and destructiveHint=false, so the description adds value by disclosing the algorithm (MLE) and output structure (parameters, conditional volatility, forecasts). No contradictions—the forecast operation aligns with read-only and idempotent hints.

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?

Three sentences, front-loaded with the essential purpose. Every sentence adds distinct value—purpose, usage guidance, and output summary—with no extraneous information.

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 tool's complexity (3 parameters, no output schema), the description effectively outlines the output (GARCH parameters, conditional volatility, forecasts) and the estimation method (MLE). It could mention assumptions like data frequency or stationarity, but it covers the core elements sufficiently.

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 parameters are already documented. The description mentions 'provide a return series', matching the required 'returns' parameter, but adds no new semantic detail about parameters beyond the schema. Baseline 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 specifies 'GARCH(1,1) volatility forecast using maximum likelihood estimation', clearly stating the verb (forecast), resource (volatility), and model type. This distinguishes it from sibling tools like 'stats_realized-volatility' or 'risk_var-parametric' that serve different purposes.

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 forecasting future volatility using a GARCH(1,1) model. Provide a return series.' It gives a clear condition for use, though it lacks explicit 'when not to use' or alternatives. Nonetheless, the guidance is direct and actionable.

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