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

With no annotations, the description carries the full behavioral burden and clearly states what will happen: repeated whitespace becomes one space and leading/trailing whitespace is removed. It could be more specific about which whitespace characters are included, but the core behavior is transparent.

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 one efficient sentence with no filler. The primary action is front-loaded, and every word contributes to understanding the tool's behavior.

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 one-parameter string transformation with no output schema and no annotations, the description is nearly complete. It omits only minor edge-case details such as the exact definition of whitespace, but the core input/output contract is clear.

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 description coverage is 0%, but the description effectively compensates by explaining exactly how the single 'input' string will be modified. It adds meaning beyond the plain string/maxLength schema, even though it does not explicitly name the parameter.

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 specific verb ('collapse repeated whitespace to one space and trim') and a clear resource (the input string). It unambiguously distinguishes this tool from siblings like detect-delimiter or normalize-boolean, which operate on different data aspects.

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 intended use is implied: call this when whitespace needs to be normalized. However, the description does not explicitly state when to prefer this over siblings or mention any exclusions, leaving the agent to infer the context.

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