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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns computed values, an interpretation, and a LaTeX formula, which gives useful output expectations. However, it does not explicitly state whether the computation has side effects, requires permissions, or handles invalid inputs, leaving some behavioral ambiguity.

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 primary action, and efficiently covers scope and return values without fluff. Every phrase contributes meaningful information.

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 tool is moderately complex with six analysis types, nested input objects, and no output schema. The description covers scope and return content but lacks examples, error behavior, or output structure guidance. It is adequate but not complete for the tool's complexity.

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 67%, and the input schema itself provides detailed per-analysis parameter examples (e.g., wilson_ci: {successes, n, confidence}). The description adds value by listing supported analysis types but adds no parameter-level detail beyond the schema, so the contribution is moderate.

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 the tool's purpose: 'Run a statistical analysis computation' and enumerates specific supported analyses (Wilson CI, binomial proportions, phase transition rates, power, Bayesian, descriptive stats, two-proportion comparisons). This distinguishes it from the generic sibling 'run_computation' and other query/search tools.

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 implies when to use the tool—whenever a supported statistical analysis is needed—but provides no explicit guidance on when not to use it or how it compares to alternatives like 'run_computation'. The supported-types list gives some context, but exclusions and decision rules are absent.

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