Descriptive Statistics

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the specific computed statistics but does not disclose return format, edge case handling, or other behavioral traits. This is acceptable given the annotations, but the description carries minimal extra behavioral context.

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 a single, front-loaded sentence that immediately states the tool's purpose and the key statistics. No redundant words or repetition of schema details.

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 simple one-parameter tool with rich annotations, a complete output schema, and a clear description of the computed metrics, the description is fully adequate. It does not need to explain return values or elaborate on siblings.

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% with the 'data' parameter described as 'Numeric observations (at least 2 values).' The description does not add additional parameter semantics beyond restating the tool's purpose, so it stays at the baseline for well-documented schemas.

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 uses a specific verb ('Compute') and resource ('descriptive statistics') with a detailed list of metrics (n, min, max, mean, etc.). This clearly distinguishes it from siblings like stats_t_test (inferential) and seq_stats (sequence statistics), making its purpose unambiguous.

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?

No explicit when-to-use or alternative tools are mentioned. The usage is implied by the summary statistics list, but there is no guidance on when to select this over inferential statistical tools. This is functional but lacks direct context.

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