parse_grouped_number

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

No annotations are provided, so the description carries the full disclosure burden. It conveys the core transform and implies a pure, side-effect-free operation, but says nothing about the return type (numeric string vs number), decimal-separator handling (Persian ٫ vs thousands ٬), negative signs, or behavior on non-numeric input.

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?

A single sentence with the transform front-loaded and an inline example that does the work of several explanatory sentences. No filler.

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 one-parameter, annotation-free utility with no output schema, the description covers what it does and gives an input example. It omits the return type and error/edge-case behavior, which an agent would need to know to chain this into a calculation pipeline.

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 single 'text' parameter has 0% schema description coverage, so the description must compensate. The example does show the expected input shape ('۲۰۰٬۰۰۰'), which is genuinely useful, but it does not state whether mixed Latin/Persian digits, whitespace, decimals, or stray characters are accepted.

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?

States a specific verb ('Strip') and resource (separators and Persian/Arabic digits) with a concrete worked example, so the transform is unmistakable. It does not name the siblings it is distinguished from (to_persian_digits, persian_words_to_number, format_number_grouped), so an agent must infer the boundary from the example rather than from explicit routing.

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?

Usage is only implied by the example: use this when you have a separated digit string that needs normalizing. There is no explicit when-to-use, no mention of the inverse tool (format_number_grouped) or of the words-vs-digits alternative (persian_words_to_number), leaving the agent to reason about selection.

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