min_float

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns a value ('Get the minimum...'), implying it's a read-only operation, but doesn't clarify if it's deterministic, has side effects, or requires specific inputs. For a tool with zero annotation coverage, this leaves significant behavioral gaps.

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 extremely concise and front-loaded: the first sentence directly states the purpose, and the second adds clarifying context ('Smallest positive normal float') and metadata ('Domain: arithmetic, Category: general'). Every sentence earns its place with no wasted words.

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 simplicity (no parameters, no annotations, no output schema), the description is adequate but minimal. It explains what the tool returns but doesn't provide examples, precision details, or comparisons to related constants. For a mathematical constant tool, more context could enhance usability, but it meets the minimum viable threshold.

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 tool has zero parameters, and the input schema has 100% description coverage (though empty). The description does not need to explain parameters, as there are none. It appropriately focuses on the tool's output semantics, so it meets the baseline for a parameterless tool.

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's purpose: 'Get the minimum positive normalized floating-point value. Smallest positive normal float.' It specifies the verb ('Get') and resource ('minimum positive normalized floating-point value'), making the action explicit. However, it does not distinguish this tool from its sibling 'max_float' or other floating-point tools, which prevents a perfect score.

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 provides no guidance on when to use this tool versus alternatives. It mentions a domain ('arithmetic') and category ('general'), but this is too vague to help an agent choose between this and similar tools like 'max_float' or 'machine_epsilon'. There are no explicit when-to-use or when-not-to-use instructions.

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