digit_sum

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 only states what the tool does, without explaining how it behaves—e.g., whether it handles negative numbers, large inputs, or invalid bases, what the output format is, or if there are any error conditions. This leaves significant gaps in understanding the tool's operational characteristics.

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, followed by domain and category in parentheses. There is no wasted verbiage or redundancy, making it efficient and easy to parse, though this brevity contributes to gaps in other dimensions.

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 lack of annotations, 0% schema description coverage, and no output schema, the description is incomplete. It fails to provide necessary context such as parameter details, behavioral traits, or output expectations. For a tool with two parameters and mathematical operations, more information is needed to ensure the agent can use it correctly without confusion.

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, meaning parameters 'n' and 'base' are undocumented in the schema. The description does not compensate by explaining what these parameters represent, their valid ranges, or examples of usage. For instance, it does not clarify that 'n' is the integer whose digits are summed or that 'base' defaults to 10, leaving the agent to guess parameter meanings.

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 sum of digits of a number in given base.' It specifies the verb ('calculate'), resource ('sum of digits'), and context ('number in given base'), making it unambiguous. However, it does not explicitly differentiate from sibling tools like 'digital_sum_sequence' or 'digit_product', which are related but distinct operations, preventing 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 no guidance on when to use this tool versus alternatives. It mentions the domain and category ('Domain: arithmetic, Category: digital_operations'), but this is too vague to inform usage decisions. There is no mention of prerequisites, typical use cases, or comparisons to siblings like 'digital_root' or 'digit_product', leaving the agent without practical usage direction.

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