radix_convert

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 an important behavioral trait: bases that cannot represent a fraction are flagged as non-terminating (e.g., 0.1 in base 2). This goes beyond the schema and helps predict tool behavior. It does not mention handling of negatives or case sensitivity, but the provided information is substantial.

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 two sentences and front-loaded with the core purpose. The behavioral note and example are concise and add value without redundancy. Every word earns its place.

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 that an output schema exists, the description does not need to explain return values. It covers the main features: any base 2..36, fractions, non-terminating flag, and provides an example. It lacks differentiation from `base_repr` and edge-case details, but for a moderately complex conversion tool, this is reasonably complete.

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?

With schema description coverage at 0%, the description must compensate. The example `radix_convert('zz', 36, 7)` clarifies the order (value, to_base, from_base) and the range 2..36. However, it does not mention that `to_base` and `from_base` default to 10, nor does it specify input constraints like string format beyond the example. Some parameter semantics are inferred but not fully explicit.

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 clearly states the tool converts a value between any bases 2..36 and includes fractions, with a concrete example. It distinguishes itself from sibling tools like base_repr by emphasizing support for fractions and arbitrary bases, so an agent can identify when to use this tool.

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 example `radix_convert('zz', 36, 7)` implies the parameter order and a use case, and the range 2..36 sets constraints. However, it does not explicitly state when to prefer this tool over `base_repr` or other siblings, nor does it describe conditions like when a conversion might fail or require special handling.

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