calculate_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. It states the tool returns statistics but does not mention any side effects, auth needs, or rate limits. Since it is a stateless calculator, the transparency is adequate but not enhanced beyond the basic function.

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 short and front-loaded with the core purpose. The sentence about list_bundles is somewhat extraneous but does not detract significantly. It could be slightly more concise without the last sentence.

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?

The description lists the computed statistics and return fields, which aligns with the output schema. For a single-parameter tool, this is sufficient. It does not cover error conditions or edge cases, but the simple nature makes it adequate.

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 already describes the only parameter 'values' as an array of numbers with minItems 1. The description adds no additional semantic meaning about the parameter, so it earns the baseline of 3 given 100% schema coverage.

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 calculates descriptive statistics (mean, median, mode, std dev, quartiles) and lists the return fields. It distinguishes itself from siblings like 'calculate_average' by offering a comprehensive set of statistics.

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 does not explicitly guide when to use this tool versus alternatives. It mentions 'See list_bundles for related education calculators' but does not contrast with siblings like 'calculate_average' or other statistical tools.

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