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

No annotations are provided, so the description carries the full burden and does so well: it discloses the return contents (coded/uncoded OLS coefficients, model_equation_uncoded, Type III vs Type I SS), automatic detection of 'Block'/'WholePlot' terms, and GLM behavior for non-normal families. It omits error conditions, data-format expectations, and the role of api_key.

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?

Dense but well ordered: purpose first, then outputs, then optional behaviors. Each sentence carries information, though the paragraph is long and mixes return-value detail with parameter guidance.

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-value detail is largely redundant, and the real gap is the undocumented majority of the input parameters (5 of 7 with 0% schema coverage). The description is adequate for calling the tool but leaves key inputs like model_terms and factor_columns unexplained.

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 7 parameters, so the description must compensate. It gives real semantics for only two (factor_info with its element shape and purpose, response_family with its enum values), leaving data, response, api_key, model_terms, and factor_columns entirely undocumented.

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 ('Run ANOVA analysis on experimental results') and details what it produces (OLS coefficients, Type III ANOVA, significance). It is clearly a DOE analysis tool, but it never distinguishes itself from close siblings such as covadoe_fit_rsm, covadoe_canonical_analysis, or covadoe_model_selection.

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 explicit when-to-use or when-not-to-use guidance, and no alternative tool is named. Usage is only implied by 'on experimental results' and the automatic blocking/split-plot handling, leaving the agent to infer when this beats fit_rsm or canonical_analysis.

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