solve_ratio

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

No annotations are provided, so the description carries the full burden. It discloses the core operation, the required input shape, and the meaning of solve_for including the 'x' shorthand. Output format is not described, but an output schema exists and this is a pure calculation tool.

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 three short, purposeful lines: condition, parameter contract, and example. It is front-loaded with 'When:' and contains no filler or redundant information.

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 calculator tool with no annotations and an output schema present, this description covers all necessary invocation details: the equation type, the known/unknown requirement, the solve_for mechanism, and a concrete example. No critical gaps remain.

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 input schema has no textual descriptions for parameters, only titles and defaults. The description compensates fully by explaining that three of a,b,c,d must be known, defining solve_for's allowed values (a|b|c|d|x), and clarifying that x means the single missing slot. The example adds further clarity.

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 explicitly defines the tool as solving a proportion a:b=c:d with one unknown, and the example confirms that behavior. This clearly distinguishes it from sibling tools like solve_linear or solve_polynomial.

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 opening 'When:' immediately states the intended use case, and the parameter contract (three knowns, solve_for target) is explicit. It does not mention when-not to use or alternatives, but the context is clear enough for correct selection.

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