digit_product

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

No annotations are provided, so the description carries full burden. It states the calculation but does not disclose behavioral traits such as error handling (e.g., for negative numbers or non-integer inputs), performance characteristics, or output format. The description is minimal and lacks necessary operational context.

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, consisting of a single sentence that directly states the tool's purpose. There is no wasted text, and it efficiently communicates the core functionality without unnecessary elaboration.

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 operation with two parameters), lack of annotations, no output schema, and 0% schema description coverage, the description is incomplete. It does not explain the return value, error conditions, or detailed parameter usage, leaving significant gaps for an AI agent to understand and invoke the tool correctly.

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?

Schema description coverage is 0%, so the description must compensate. It mentions 'number' and 'base' implicitly in the calculation context but does not explain parameter meanings beyond the schema's type definitions. The description adds marginal value by hinting at the base parameter's role, but it does not fully document the two parameters' semantics or usage.

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 the product of digits of a number.' It specifies the verb ('calculate'), resource ('product of digits'), and includes domain/category context. However, it does not explicitly differentiate from sibling tools like 'digital_product_sequence' or 'digit_sum', which are related but distinct operations.

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 and category but gives no explicit when/when-not instructions or references to sibling tools. Usage is implied by the description's content but lacks actionable context for an AI agent.

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