arima_model_diagnostics

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

With no annotations, the description carries the transparency burden and does a solid job: it enumerates the diagnostic tests performed and states the return format as 'text analysis and ImageContent with diagnostic plots.' It does not fully clarify whether the tool trains the model internally or requires a previously trained model, but the core behavior is well disclosed.

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 well-structured with a one-line purpose, a 'Provides' bullet list, an Args section, and a Returns section. It is front-loaded and every section earns its place without unnecessary verbosity.

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?

The description covers parameters and return values, but it leaves a notable gap: it does not explain the relationship to train_arima_model or whether the user must train a model first. Since there is no output schema and no annotations, this ambiguity about the model prerequisite makes the definition incomplete for confident invocation.

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 0%, so the description must compensate, and it does. It explains all three parameters with concrete examples: ticker symbols, allowed period values, and transform options including None. This adds meaning well beyond the bare input 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 specific verb and resource: 'Perform comprehensive diagnostics on trained ARIMA model.' The bullet list of tests (residual analysis, normality tests, Ljung-Box, adequacy checks) clearly distinguishes it from siblings like train_arima_model and forecast_arima_model.

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 phrase 'on trained ARIMA model' implies the tool is used after model training, and the diagnostics content suggests it is not for forecasting or training. However, it never explicitly says when to use it versus train_arima_model or forecast_arima_model, nor does it state any exclusions.

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