test_seasonal_simplification

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

With no annotations, the description carries the full burden. It discloses the statistical model being fit (restricted model with harmonics fixed to zero), the test statistic formula, degrees of freedom, and interpretation critera. It does not explicitly state whether the tool writes results to disk or purely returns a value, but the name and context imply a read-only test. The statistical detail is substantial and transparent.

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 sections for definition, computation, workflow, and parameters. It is moderately long but every sentence adds value—the formula is essential, the workflow is actionable, and the parameter list is clear. No filler or redundancy.

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 purpose, methodology, workflow, and all parameters. The only gap is that it does not explicitly describe the return value structure (e.g., whether it returns a p-value, LR statistic, or a decision). Since there is no output schema, the description should ideally state what the tool returns. The interpretation guidance makes this implicit but not fully explicit.

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 thoroughly. The Parameters section clearly explains inp_path ('path to a fitted .inp or .pre file'), freq_list ('harmonic indices to test', with the default behavior 'None = test all harmonics jointly'), and alpha ('significance level (default 0.05)'). The workflow also adds practical meaning to freq_list by telling users to pass t-value-selected harmonics.

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 'Joint LR test for eliminating seasonal harmonics: H₀: cos_k = sin_k = 0.' This is a specific verb (test) plus resource (seasonal harmonics) and clearly states the null hypothesis. It distinguishes itself from sibling tools by focusing on simplification testing and explicitly referencing seasonal_param_analysis as the preceding workflow step.

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 provides explicit workflow guidance: 'Typical workflow after seasonal_param_analysis' and then gives concrete instructions on which frequencies to pass (k values with |t| ≤ 2) and how to interpret the result (remove vs. keep harmonics). This clearly tells the agent when to use this tool and what to do with its output.

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