arima

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

No annotations are provided, so the description carries the full burden. It usefully discloses validation constraints not present in the schema ('forecast_steps' 0..10000, 'conf_alpha' in (0,1)), but says nothing about return values, model-fitting behavior, or failure modes for a compute 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?

Front-loads the purpose in the first sentence, then adds input-format and constraint details. Every sentence earns its place, though the constraint sentence could be slightly tighter.

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, no annotations, and 5 parameters, the description is only partially complete. It covers the series input and two constraints but omits the meaning of 'order'/'seasonal_order' and any indication of what a fit or forecast returns.

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 the description must compensate. It clarifies the non-obvious 'series' input (inline list vs '@<path>' file reference) and bounds two numeric params, but leaves 'order' and 'seasonal_order' entirely unexplained despite their being the core ARIMA specification (p,d,q).

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 verb and resource: 'Fit an ARIMA model and optionally forecast.' An agent can immediately tell this is ARIMA-specific modeling rather than generic forecasting. However, it does not explicitly differentiate itself from the sibling 'forecast' tool, so it falls short of a 5.

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?

There is no guidance on when to use this versus alternatives like 'forecast', 'time_series_decompose', or 'stationarity_test'. The 'optionally forecast' phrase implies a use case but no conditions, prerequisites, or exclusions are stated.

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