time_series

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It transparently states that the model is automatically selected between ARIMA and ETS, enumerates the diagnostics performed (ADF, KPSS, STL, Ljung-Box), and notes that plots are generated. It does not mention that save_as can create a session object, but that is documented in the input schema, so the description adds meaningful behavioral context beyond the structured data.

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 two sentences and front-loads the main behavior before the frequency instruction. The first sentence is a long list, but every listed item is meaningful, and the second sentence provides an actionable parameter-setting rule that is worth its place. There is no fluff or repetition of schema content.

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?

This is a complex 11-parameter tool with no output schema, so the description must communicate what the agent will receive. It does so by itemizing the stationarity tests, decomposition, selected model, in-sample accuracy, residual test, forecast intervals, and plots. It could additionally note that plots can be suppressed or that save_as persists a ts object, but those are already covered by parameter descriptions and the main call is otherwise fully specified.

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?

Since every parameter already has a schema description, the baseline is 3. The tool description adds value by translating the frequency parameter into concrete real-world examples: 12 for monthly, 4 for quarterly, and 7 for daily data with a weekly cycle. It does not add prose guidance for method or h, but those are already well described in the 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?

The description opens with a clear domain and operation, 'Time series analysis and forecasting,' then enumerates a specific set of statistical outputs and plot types. This makes the tool's purpose unambiguous and separates it from general-purpose siblings like describe, plot, or data_inspect. It lacks an explicit action verb like 'performs' and does not name a sibling alternative, but the output inventory is specific enough to identify what the tool does.

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 intended context is clearly time-series data, and the closing sentence provides concrete guidance about setting the frequency parameter for monthly, quarterly, and daily-weekly data. The description does not explicitly state when to avoid this tool or which sibling to use instead, but none of the siblings offer forecasting or stationarity testing, so the absence of exclusions is a minor gap.

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