pollard_rho

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

No annotations exist, so the description carries the full burden. It usefully discloses the algorithm family (Pollard's rho plus Miller-Rabin), which hints at probabilistic behavior and suitability for large integers, but it says nothing about the return shape, failure modes for non-positive or non-integer input, or performance 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?

A single compact sentence with the action and method front-loaded; no padding or 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?

With no output schema and no annotations, the description should ideally indicate what comes back (e.g., a list of prime factors or a factorization) and any input limits. As written it is adequate for a simple math utility but leaves return semantics and edge cases to inference.

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 coverage is 100%, so the single 'value' parameter is already documented as a positive integer in the schema. The description adds no format, range, or constraint detail beyond that, which is the expected baseline when the schema does the work.

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?

States a specific verb (Factor) and resource (an integer) and names the algorithmic approach (Pollard's rho with Miller-Rabin). It does not, however, explicitly distinguish itself from close siblings like prime_factor or miller_rabin_test, leaving the agent to infer the difference.

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?

There is no guidance on when to choose this over prime_factor, duval_factorize, or miller_rabin_test, nor any note on input constraints such as size limits or expected runtime. The agent must guess from the name alone.

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