float_repr

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

With no annotations, the description carries full responsibility for explaining behavior. It discloses the exact outputs (value, bits, ULP, neighbors), the representability check, and a warning about the 2^53 threshold. This is rich, honest behavioral disclosure.

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 concise and well-structured: a clear summary sentence, followed by illustrative examples, and a critical edge-case warning. Every sentence adds value without repetition or fluff.

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 the tool's complexity and minimal schema, the description is remarkably complete. It covers the main outputs, demonstrates behavior with examples, and highlights a key precision threshold. Since an output schema exists, the absence of return format details is acceptable.

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 schema only indicates a required number 'x' with 0% description coverage. The description names X as the value being inspected and provides examples, but it does not elaborate on input constraints, special values, or formatting. It partially compensates, but not fully.

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 what the tool does: it reveals what binary64 actually stores for a given value, including exact value, raw bits, ULP, neighbors, and representability. Concrete examples with 0.1 and 0.25 make the purpose unmistakable and distinguish it from sibling tools like base_repr or radix_convert.

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 implies when to use the tool: whenever you need to inspect the binary64 representation of a number. It gives clear context and examples, but it does not explicitly mention alternatives or state when not to use it, stopping short of full usage guidance.

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