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

No annotations are provided, and the description does not disclose behavioral traits like error handling for empty inputs, tie-breaking for median/mode, or performance constraints. It only lists the statistics computed, lacking important context for reliable operation.

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 one main sentence and a helpful pointer to list_bundles. It is not overly verbose, but could be better structured with line breaks or bullet points for the return fields.

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 presence of an output schema (though not shown), the description lists return fields sufficiently. However, it omits details like handling multiple modes, data validation beyond minItems=1, and assumptions about the input distribution. Adequate for basic use but not fully comprehensive.

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?

The input schema covers 100% of parameter descriptions, so the description adds no additional meaning. However, the schema itself provides adequate information for the 'values' array. The tool description does not enhance understanding beyond the schema.

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 it calculates descriptive statistics (mean, median, mode, std dev, quartiles) and lists specific return fields. It distinguishes from related calculators by referencing list_bundles for 'education' calculators, but does not directly differentiate from siblings like calculate_average or calculate_percentile_rank.

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 explicit usage guidance, such as when to use this tool versus alternatives or when not to use it. The reference to list_bundles is indirect and does not clarify optimal scenarios.

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