statistics

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds a note about data_y behavior, but does not disclose side effects, edge cases, or other operational details. It does not contradict annotations, and with annotations present, a 3 is appropriate.

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 two sentences, front-loaded with the main purpose, and every word contributes. It lists operations in a compact list and adds a single actionable note about data_y, with no fluff or repetition.

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 that an output schema exists and annotations cover safety, the description provides sufficient context for the core operations and the key data_y nuance. It is slightly thin on optional parameters (precision, confidence), but their defaults in the schema mitigate the gap, making it generally complete for a read-only statistical tool.

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. It clarifies that data_y is used for paired correlation/regression or the second t-test sample, adding meaning beyond the bare schema. However, it does not explain precision or confidence parameters, which remain undocumented in both schema and description, so compensation is incomplete.

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 explicitly lists the specific statistical operations (descriptive statistics, Pearson correlation, linear regression, confidence interval, Welch t-test) with a clear verb 'Compute'. It distinguishes itself from sibling tools by naming statistical tests, but does not explicitly contrast with non-statistical tools like calculate or linear_algebra, so it lacks full sibling differentiation.

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 second sentence provides concrete guidance on when to supply data_y ('for paired correlation/regression or the second t-test sample'), which helps usage. It implies the tool is for statistics but does not explicitly state when not to use it or name alternatives, but the context is clear enough for a 4.

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