check_assumptions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It enumerates the exact tests and plots produced (Shapiro-Wilk, Levene, Q-Q plots, VIF, etc.) and clearly separates raw-data vs fitted-model behavior. It does not explicitly state that the tool is read-only or what the return structure looks like, but the disclosed behavior is substantial.

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, front-loaded with the core purpose, and organized by semicolons to separate raw-data and model contexts. Every clause carries information; there is no filler or repetition of parameter names.

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?

For a tool with 8 optional parameters, no annotations, and no output schema, the description does a strong job covering both operation modes, the relevant statistical checks, and the role of `plots`. It lacks a brief note on the return value/result object, but this is not critical for selecting and invoking the 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 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining the mode-switching relationship between `model` and `variables`, and by grouping checks by data vs model. This is meaningful added value, justifying a 4.

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 uses a specific verb ('Check') and resource ('assumptions behind parametric tests'), and clearly distinguishes two modes: raw data and fitted model. It does not explicitly differentiate from the sibling tool 'model_diagnostics', which could overlap with the fitted-model checks, so it stops 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?

The description gives clear context for when to use each mode ('On raw data' vs 'On a fitted model') and even instructs to pass `model` instead of `variables`. It does not, however, mention when not to use this tool or name alternative sibling tools, so it lacks explicit exclusions.

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