forecast_timeseries

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

No annotations provided, so description carries full burden. It discloses return format (point forecast + approximate 80% CI) and behavioral traits (seasonal models fall back to trend-only if data insufficient). No destructive or auth concerns for forecast tool.

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?

Description is well-structured with Args and Returns sections. Front-loaded with purpose. Some redundancy in repeating parameter descriptions, but overall efficient. Every sentence adds value.

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 3 parameters (2 required) and existing output schema, description covers all necessary aspects: input constraints, seasonal fallback, return format. Agent has enough information to use tool correctly.

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 0%, so description fully explains parameters. 'values' specifies chronological order and minimum 4 points; 'horizon' is number of steps; 'seasonal_periods' explains its meaning and usage. Adds meaning beyond types and titles in schema.

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?

Description clearly states the tool forecasts future values of a univariate time series. The action (forecast) and resource (time series) are specific and unambiguous. No sibling tools exist, so differentiation is not required.

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?

Description provides usage context: minimum 4 data points, seasonal behavior and fallback logic. It advises when to omit seasonal_periods (if unknown or too short). While no explicit when-not-to-use is given, the guidance is sufficient for proper invocation.

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