statistics

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions what the tool does but lacks details on traits like error handling (e.g., for empty arrays or invalid operations), performance (e.g., computational limits), or output format (since no output schema exists). This is a significant gap for a tool with no structured safety or behavioral hints.

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, efficient sentence that front-loads the purpose and lists key operations without unnecessary words. Every part earns its place by specifying the action, measures, and input source, making it easy to scan and understand quickly.

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?

Given the tool's complexity (statistical operations with two parameters) and lack of annotations and output schema, the description is incomplete. It does not explain return values, error conditions, or behavioral constraints, which are crucial for an AI agent to use the tool correctly. More context is needed to compensate for missing structured data.

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 100%, so the schema already documents both parameters ('numbers' as an array and 'operation' with enum values). The description adds minimal value beyond the schema by implying the tool uses the numbers for statistical analysis, but it does not provide additional syntax, format details, or examples. Baseline 3 is appropriate when the schema handles most documentation.

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 specific action ('calculate statistical measures') and resources ('from a list of numbers'), listing the exact operations (mean, median, mode, standard deviation, variance). It distinguishes from sibling tools like 'calculate' (generic), 'convert_units', 'percentage', etc., by specifying statistical analysis rather than general calculations or other mathematical functions.

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 versus alternatives like 'calculate' or other mathematical tools. It does not mention prerequisites (e.g., needing numeric data), exclusions (e.g., non-numeric inputs), or specific contexts where statistical analysis is preferred over other calculations, leaving usage decisions ambiguous.

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