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

No annotations are present, so the description carries the full burden of behavioral disclosure. It does not state what values are returned (e.g., min, Q1, median, Q3, max), how the dataset is supplied given the empty input schema, or how edge cases are handled.

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 but under-specified, essentially restating the tool name: 'Five Number Summary: Five-number summary of a dataset.' The repetition adds no practical information beyond the name itself.

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?

With no annotations, no output schema, and an empty input schema, the description must convey all operational context, but it only restates the concept. An agent cannot determine the required input, the return format, or how this differs from other statistics tools.

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 has zero parameters and is fully self-describing, so there are no parameter details for the description to clarify. The description could still explain how the dataset is provided, but the schema already exhaustively covers the parameter surface.

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 states a clear operation: computing a five-number summary of a dataset. It does not differentiate the tool from overlapping siblings like get_stats or stats, and it never defines what numbers constitute the five-number summary.

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?

There is no guidance on when to use this tool versus related statistical siblings such as quartile, median, get_stats, or stats. An agent cannot tell when this tool is the right choice for a given request.

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