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

No annotations, so the description carries the full behavioral burden. It usefully discloses the sn_type/response_goal inference logic and the shape of the output, but says nothing about required data structure, permissions, or computational limits. Adds real value but leaves notable behavioral gaps.

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?

Three tight sentences, purpose front-loaded before the parameter and return details. No filler; every clause carries information.

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 presence of an output schema makes the return-value listing somewhat redundant, while the 6-parameter tool with 0% schema description coverage still leaves the data/response/factor_columns contract underspecified. Adequate but with clear gaps for a tool this parameter-heavy.

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 explains sn_type values and its fallback to response_goal, which is genuinely useful, but leaves data, response, factor_columns, and api_key entirely undocumented in both schema and description.

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 method (Taguchi robust-design analysis) and the concrete computation (S/N ratios + main effects). The Method name is unique among the covadoe_ sibling set (analyze, fit_rsm, optimize), so an agent can distinguish it without opening the schema.

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 conveys that this is for Taguchi designs, implying usage context, but never states when to pick this over covadoe_analyze or covadoe_fit_rsm, nor any prerequisite about the design/data format. Usage must be inferred from the method name alone.

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