root_find

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

Annotations already declare the tool as read-only, idempotent, non-destructive, and open-world. The description adds little behavioral context beyond restating the method options already present in the schema; it does not mention convergence behavior, failure modes, derivative requirements, or the nature of the returned root.

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 a single front-loaded sentence with no extraneous words. It is concise, though its terseness leaves important usage and behavioral details unstated.

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?

Given seven parameters and no output schema, the description is minimally adequate but incomplete. The schema and annotations cover parameter definitions and safety, but the description does not explain return semantics, convergence, or method-specific requirements that an agent might need.

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%, so every parameter is already documented in the schema. The description adds no additional meaning about parameter relationships or formats beyond what the schema provides, making the baseline 3 appropriate.

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 gives a specific verb and resource ('Find roots of a math expression') and names the supported algorithms. It is clear enough to distinguish from siblings like nth_root, though it does not explicitly contrast itself with them.

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 names Newton's method and bisection, giving implied usage scenarios, but it does not explain when to choose one over the other or the prerequisites for each (e.g., bounds for bisection, initial guess for Newton).

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