statistics

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

No annotations are provided, and the description does not mention side effects, error handling, or return behavior. For a computational tool, this is acceptable but minimal; basic transparency about potential errors or output format is missing.

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 dense but efficient, front-loading the overall purpose. Every sentence adds value, though it could be broken into sections for readability. It is not excessively long.

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 lacks specification of return values or output format. For 'linear_regression', it mentions input but not output. Given the complexity of operations and no output schema, more completeness is needed, though many ops have implied returns.

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?

The input schema has 0% parameter descriptions, but the description adds significant meaning: it explains each op, what 'data' and 'args' are used for, and specific constraints for many operations (e.g., 'normal_pdf needs {x, mean, std}'). This fully compensates for the missing schema descriptions.

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 clearly states that the tool computes descriptive statistics and probability distributions, listing all operations with their required parameters. It distinguishes between two categories (descriptive ops using 'data' and distribution ops using 'args'), and the op enum is exhaustive.

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 clear context per operation, including constraints like 'quantile needs args.prob' and 'skewness needs n≥3'. However, it does not explicitly state when to avoid this tool or name alternatives, though sibling tools are different domains.

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