atanh

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 'domain validation,' which implies input constraints (e.g., x must be in (-1, 1) for real-valued results), but doesn't detail what happens on invalid input (e.g., error handling, return values). It also omits other behavioral traits like performance, precision, or output format, making it insufficient for a mutation-free but constraint-sensitive tool.

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 highly concise and front-loaded: the first part states the core functionality, and the parenthetical adds domain and category context. Every sentence earns its place with no redundant information, making it efficient and easy to parse for an AI agent.

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 low complexity (single parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and hints at constraints but lacks details on domain specifics, error handling, or output values. For a mathematical function with potential input restrictions, more completeness would be beneficial, but it's not entirely inadequate.

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 context by implying 'x' is the input for the inverse hyperbolic tangent function and mentions 'domain validation,' suggesting constraints on 'x.' However, it doesn't specify the exact domain (e.g., |x| < 1) or validation behavior, leaving gaps. This provides some meaning beyond the schema but not fully compensatory.

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 tangent with domain validation.' It specifies the verb ('calculate'), resource ('inverse hyperbolic tangent'), and constraint ('domain validation'), distinguishing it from generic math tools. However, it doesn't explicitly differentiate from sibling tools like 'atan' or 'atan2', which are related but not identical.

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 the domain ('trigonometry') and category ('inverse_hyperbolic'), but doesn't specify scenarios, prerequisites, or exclusions. For example, it doesn't clarify when to choose 'atanh' over 'atan' or other inverse hyperbolic functions, leaving usage context implied at best.

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