calculate_z_score

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

The description mentions return values (z, percentile, p-value) but does not disclose potential edge cases (e.g., std_dev ≈ 0) or performance characteristics. With no annotations, the description is adequate but minimal.

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, no redundant information, and critical details (purpose, usage, returns, related tools) are front-loaded and concise.

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?

For a computation tool with output schema, the description covers key aspects: inputs, outputs, and usage context. Minor omissions like distribution assumption or p-value type are acceptable for typical use.

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?

Input schema has 100% coverage with descriptions, so the baseline is 3. The description merely restates parameter names without adding new meaning or constraints.

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 computes z-score and percentile, specifies inputs (value, mean, std_dev), and explains usage in statistics and outlier detection, distinguishing it from numerous sibling calculators.

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 context (statistics and outlier detection) and references list_bundles for related tools, giving implicit guidance. However, it lacks explicit when-not-to-use or exclusions.

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