divisor_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 states the tool calculates the divisor sum but does not mention any behavioral traits such as input constraints (e.g., handling of negative numbers, large integers), performance characteristics, or error handling. This leaves significant gaps for a tool with no annotation coverage.

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, stating the core purpose in a single sentence with additional domain/category context. Every part earns its place, though it could be slightly more informative without sacrificing brevity.

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 of a mathematical function with no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It does not explain what the tool returns (e.g., integer sum), handle edge cases, or provide usage examples, making it inadequate for full understanding.

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, and the description does not add any parameter-specific information beyond implying 'n' is a number. It does not clarify the meaning of 'n' (e.g., positive integer, natural number) or any constraints. With low schema coverage, the description fails to compensate adequately, resulting in a baseline score.

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 with a specific verb ('calculate') and resource ('sum of all positive divisors of a number'), and it distinguishes itself by naming the divisor function σ(n). It also includes domain and category context, which helps differentiate it from sibling tools like 'divisor_count' or 'divisors'.

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 implies usage in arithmetic or number theory contexts but does not explicitly state when to use this tool versus alternatives like 'divisor_count' or 'divisors'. It provides some contextual guidance through the domain and category, but lacks explicit when/when-not instructions or named alternatives.

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