acoth

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 mentions 'domain validation', which implies input constraints (likely x ≠ 0 for inverse hyperbolic cotangent) and error handling, adding useful behavioral context. However, it doesn't disclose other traits like performance, error messages, or output format, leaving gaps for a tool with no structured annotations.

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 front-loaded, consisting of a single sentence that states the core functionality and adds domain/category context. There's no wasted verbiage, and every part contributes to understanding the tool's purpose, though it could be slightly more informative without losing efficiency.

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 mathematical nature, 1 parameter, no annotations, and no output schema, the description is incomplete. It mentions domain validation but doesn't detail the domain constraints, error behavior, or return values. For a tool performing a specialized calculation, more context on inputs and outputs is needed to guide the agent effectively.

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 (x) with 0% description coverage, and the tool description adds no information about the parameter. It doesn't explain what 'x' represents (e.g., a real number, domain restrictions like |x| > 1 for acoth), its units, or valid ranges beyond the vague 'domain validation'. This fails to compensate for the low schema coverage.

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: 'Calculate inverse hyperbolic cotangent with domain validation.' It specifies the verb ('calculate'), resource ('inverse hyperbolic cotangent'), and includes domain validation as an additional feature. However, it doesn't explicitly differentiate from sibling tools like 'acoth' vs 'acot' or 'acsch', though the domain/category hints help.

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 domain validation and categorizes it under 'trigonometry' and 'inverse_hyperbolic', but doesn't specify use cases, prerequisites, or compare it to related siblings like 'acot' or 'acsch'. This leaves the agent without explicit usage instructions.

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