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

No annotations provided, so description carries full burden. It explains key behaviors: number as string to preserve trailing zeros, standard sig fig rules, and return of scientific notation. It does not mention any destructiveness or auth needs, which are irrelevant.

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?

Two sentences: first covers main functionality, second adds rules and return value, third provides usage context. No wasted words, front-loaded with purpose.

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?

With only two parameters, full schema coverage, and an output schema, the description is complete. It covers purpose, parameters, behavior, and usage without gaps.

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%, but description adds meaning beyond schema: explains why number should be a string (preserve trailing zeros) and that rounding is optional. The example '1.200' clarifies trailing zero significance.

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?

Description clearly states the verb 'Count' and 'round' with resource 'significant figures'. It specifies that it accepts a number as a string and optionally rounds, which distinguishes it from sibling tools like scientific_notation or other calculators.

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?

Explicitly lists use cases: 'laboratory measurements, error analysis, and maintaining proper precision in chained calculations.' While it doesn't state when not to use, the context is clear and helpful.

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