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

The description adds significant value beyond annotations: it explicitly states the tool is read-only and idempotent (matching annotations), and crucially discloses error behavior ('never raises a protocol error — it returns {"error": ...}'). This behavioral detail would not be inferred from annotations alone.

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 extremely concise (three paragraphs) with no wasted words. It front-loads the purpose, then provides a clear example, usage guidelines, and error handling. Every sentence adds value.

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 and the tool's moderate complexity, the description covers all necessary aspects: input, output, usage scope, limitations, error handling, and idempotency. An agent has full context to correctly invoke and interpret results.

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 coverage is 100% and the parameter 'numbers' is already described in the schema. The description enhances meaning by showing a realistic input example and explaining the output structure (center, spread, quartiles, outliers). This provides context beyond the schema's raw type constraint.

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's purpose with a specific verb-resource combination ('Describe a numeric dataset: center, spread, quartiles, and outliers'). It provides a typical input and output example, and distinguishes from siblings by explicitly naming alternative tools (confidence_interval, sample_size) for different tasks.

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?

Explicit guidance on when to use ('first summary of one numeric dataset') and when not to use ('Not for interval estimates... not for planning a study...'). Also explains error handling and retry safety, giving clear context for invocation.

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