acosh

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 mentions the input constraint (value ≥1), which is useful, but doesn't describe what happens if the constraint is violated (e.g., error handling), the return format (e.g., numeric output), or any performance characteristics. For a mathematical function with no annotation coverage, this is insufficient.

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—a single sentence that states the purpose and constraint without any fluff. It's front-loaded with the core functionality, making it easy to parse. Every word earns its place, and there's no wasted text.

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 complexity (a mathematical function with no annotations and no output schema), the description is incomplete. It lacks details on return values, error conditions, and behavioral nuances. While it covers the basic purpose and input constraint, it doesn't provide enough context for reliable tool invocation in varied scenarios.

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 1 parameter with 0% description coverage, so the description must compensate. It adds meaning by specifying the parameter constraint (value must be ≥1), which clarifies the semantics beyond the schema's type definition. However, it doesn't explain the parameter's role (e.g., that 'value' is the number to compute acosh for) or provide examples, leaving some ambiguity.

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: '计算反双曲余弦值' (compute inverse hyperbolic cosine). It specifies the verb (compute) and resource (inverse hyperbolic cosine), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'cosh' (hyperbolic cosine) or 'acos' (inverse cosine), which would be needed for 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 a constraint ('输入值必须≥1' - input value must be ≥1), which gives some usage context. However, it doesn't explain when to use this tool versus alternatives like 'cosh' or 'acos', nor does it mention any prerequisites or exclusions beyond the numeric constraint. This leaves gaps in guiding the agent on tool selection.

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