factorize

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

With no annotations, the description discloses the output behavior via 'factors with multiplicity' and states the input constraints (positive integer, ≤10 digits). It does not mention error handling, but the presence of an output schema and the tool's simple nature reduce the need for that detail.

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 compact and well-structured into 'When', 'Params', and 'Example' sections. Every sentence contributes useful information without redundancy.

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?

For a simple one-parameter tool with an output schema, the description covers purpose, constraints, and output semantics. The example n=12 further clarifies expected behavior, making the description complete.

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 only defines n as a number, but the description explains it must be a positive integer up to 10 digits. This adds critical semantic meaning that the schema lacks, fully compensating for the 0% schema description coverage.

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 performs prime factorization of a positive integer with multiplicity. It also explicitly distinguishes itself from evaluating fact(), which prevents confusion with factorial 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 includes a 'When:' clause specifying the intended use case and notes 'not evaluate fact()' as an explicit when-not. It does not enumerate alternative sibling tools, but provides sufficient guidance for when to choose this tool.

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