descriptive_statistics

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

No annotations are provided, so the description should fully disclose behavior. However, it does not mention handling of invalid inputs, edge cases (e.g., empty arrays, non-numeric values), or the return format (though an output schema exists). It lacks transparency about potential errors or limitations.

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 concise with a clear purpose statement, a bullet-like list of statistics, and a parameter explanation. It is front-loaded but could be more structured with clear labeling of sections.

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 tool with one parameter and an existing output schema, the description adequately covers what the tool does and how to provide input. It lists the computed statistics, though it omits details like output format for quartiles. Overall sufficient.

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?

Despite 0% schema description coverage, the tool description adds meaning to the 'numbers' parameter by specifying it accepts comma-separated numbers or a JSON array. This clarifies the input format beyond the schema's bare type 'string'.

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 'Calculate' and lists the resource 'descriptive statistics' including a clear enumeration of outputs (mean, median, mode, etc.). It distinguishes itself from siblings by being a general stats tool, not a specialized one like calculate_correlation.

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 no guidance on when to use this tool over alternatives, no exclusions or prerequisites. It simply states what it does without context for selection among many similar statistical tools.

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