stats

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

Beyond the read-only/idempotent annotations, the description reveals important behavior: it returns a 'neutral one-line interpretation', reports p_value and effect size as given, states significance only, forbids causal claims, and requires citing as its `cite`. It also warns that dataset columns use every row, not a sample. This is substantial added transparency with no contradiction.

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 front-loaded with the purpose and then uses a structured, scannable list of tests and data modes. It is longer than average, and the bilingual keyword list is somewhat redundant, but every substantive section supports invocation and interpretation.

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?

Given 13 parameters, no output schema, and no parameter descriptions in the schema, this description is unusually complete: it covers test selection, data input modes, row filtering, group_by constraints, output/interpretation expectations, and citation. It does not detail exact return formatting or distinguish itself from data_describe, but the essential information for correct use is present.

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 carry the burden, and it does. It maps test names to their parameters, e.g. ttest_1samp (vs mu), fisher_exact (data = table with an example), linregress (x in data/column, y in data2/column2), and proportion_ci/binom_test (successes, trials, confidence/p0). It also explains dataset + column (+ column2), group_by, and where.

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 first sentence states the tool's function precisely: 'Descriptive statistics and hypothesis tests (SciPy), with a neutral one-line interpretation.' The body then names all supported tests, such as ttest_1samp, mannwhitneyu, fisher_exact, pearson, and linregress, making it easy to distinguish from arithmetic, date, and data-list siblings.

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 operational context: when to use inline data versus a registered dataset, how group_by works for two-sample tests, how where filters rows, and which parameters each test expects. It does not explicitly name alternatives or when-not-to-use conditions, but the context is strong enough to guide selection.

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