stats_distribution-fit

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

Description discloses return values: best-fit distribution, parameters (mean, std), goodness-of-fit statistics (KS test, chi-squared), and Q-Q plot data. Annotations already indicate readOnlyHint=true and idempotentHint=true, and the description adds specific behavioral context beyond annotations without 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?

Two sentences that are front-loaded: first sentence states purpose and ranking, second gives use case and outputs. No wasted words.

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?

Description covers distribution types, required input, and all outputs (parameters, statistics, Q-Q plot). No output schema exists, so description must carry the burden, which it does adequately for a single-parameter 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 coverage is 100% for the single parameter 'data', with description 'Array of data to fit distributions to'. The description adds minimal extra meaning beyond the schema, so baseline 3 is appropriate.

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?

Description clearly states the verb 'fit data to common distributions' and specifies the resource (data array) and ranking by goodness of fit. It distinguishes from sibling tools like stats_normal-distribution by focusing on multiple distributions and ranking.

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?

Description explicitly says 'Use when fitting data to standard distributions (normal, lognormal, uniform)' and instructs to provide a data array. It lacks explicit when-not-to-use or alternatives, but the guidance is clear and sufficient.

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