standard_form

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

No annotations are provided, so the description carries the full behavioral burden. It mentions two output formats but never says whether both are returned in one call or how they are selected, nor what the returned string format looks like (exponent style, precision). For a zero-annotation tool this leaves key behavior unspecified.

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?

One short sentence with no filler, and the action is front-loaded. Nothing to trim.

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 trivial single-parameter conversion with no output schema, the description is minimally sufficient, but it omits the one genuinely useful detail: whether scientific and engineering notation are both returned or how to request each. With no annotations and no output schema, that gap is noticeable.

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 100% and the single parameter is self-explanatory ('Number to convert.'), so the baseline of 3 applies. The description adds no meaning beyond the schema, but the schema already does the work.

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?

Specific verb ('Convert') plus a clear resource ('a number') and named output formats ('scientific and engineering notation'). It does not, however, distinguish itself from nearby numeric-conversion siblings such as number_base_convert, base_convert, or the many math helpers, leaving ambiguity about which notation tool to pick.

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 states only what the tool does, with no indication of when to use it versus alternatives or any prerequisites on the input (e.g., valid ranges, negative numbers, zero). An agent gets no routing guidance.

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