stats_stats_normalize

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

The description describes the behavioral outcomes of each method (range or mean/std), which is valuable given no annotations. It lacks warnings about edge cases (e.g., empty array, constant values for zscore) but is sufficient for typical use.

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 (two sentences) with front-loaded purpose and method details. Every word adds value, no redundancy.

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?

For a simple normalization tool, the description covers the essential behavior. Minor gaps: values type not specified, default method not stated. Presence of an output schema reduces the need for return value description.

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?

With 0% schema coverage, the description adds critical meaning to the method parameter by explaining both options. However, it does not clarify that values should be numeric or the default method when null, leaving some ambiguity.

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 normalizes values and specifies two distinct methods (minmax and zscore) with their effects, making the purpose unambiguous.

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 implies usage when normalization is needed but does not provide guidance on when to choose minmax vs zscore or contrast with other stats tools like correlation or histogram.

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