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

No annotations are provided, so the description carries the full behavioral burden. It lists the diagnostic statistics but says nothing about what inputs are required to run them (a fitted model? raw data plus terms?), whether anything is mutated, or what conditions cause failure. Listing test names is surface-level, not behavioral disclosure.

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?

A single, front-loaded sentence that wastes no words. It is efficient, though its brevity comes partly from omitting necessary information rather than from disciplined editing.

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?

An output schema exists, so return values need not be described. However, for a 5-parameter statistical tool with 0% schema coverage and no annotations, the definition omits how to supply the model/data, what model_terms controls, and what factor_columns does. Key information an agent needs to call this correctly is missing.

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% across 5 parameters, so the description must compensate and does not. It never mentions data, response, model_terms, factor_columns, or api_key. At best, the mention of Cook's distance and leverage implicitly implies a regression model with a response and terms, but this is weak inference rather than parameter documentation.

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?

Names the resource (residual diagnostics) and enumerates the specific analyses performed (Shapiro-Wilk normality, Cook's distance, leverage). This clearly distinguishes it from modeling siblings like covadoe_fit_rsm or covadoe_ridge_analysis, which build models rather than diagnose them. It lacks an explicit verb (compute/run), but the content is specific enough to act on.

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 about when to use this tool versus alternatives such as covadoe_model_selection or covadoe_analyze, and no stated prerequisites (e.g., that it operates on an existing fitted linear model). The agent must infer the entire workflow context.

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