product

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 states the tool calculates and returns a product, implying a read-only operation, but does not disclose behavioral traits such as error handling (e.g., for empty lists or non-numeric strings), performance considerations, or output format details. This leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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, with two sentences that directly state the tool's function and return value. The additional domain and category information is brief and non-redundant. However, the lack of detail reduces overall helpfulness, though it remains structurally efficient.

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 (one parameter, no output schema, no annotations), the description is incomplete. It does not explain the return value format (e.g., numeric type), error conditions, or how the input strings are interpreted. For a tool with undocumented parameters and no annotations, more context is needed to ensure reliable use by an agent.

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 0% description coverage, with one parameter 'numbers' of type array of strings. The description adds minimal semantics by implying the list contains numbers, but does not clarify the expected format (e.g., numeric strings), constraints (e.g., minimum list length), or handling of non-numeric inputs. This fails to compensate for the low schema coverage, leaving parameter meaning unclear.

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 all numbers in a list. Returns the multiplication of all elements.' It specifies the verb ('calculate'), resource ('product of all numbers in a list'), and outcome. However, it does not explicitly differentiate from sibling tools like 'multiply' or 'element_wise_multiply', which might handle similar operations differently.

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 ('arithmetic') and category ('general'), but does not specify contexts, prerequisites, or comparisons to sibling tools like 'multiply' or 'element_wise_multiply', leaving 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.