strong_pseudoprime_test

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's function but does not describe what the test returns (e.g., boolean result, details on pseudoprimality), performance characteristics, error handling, or limitations (e.g., input constraints, computational complexity). For a tool with no annotations, this leaves significant behavioral gaps.

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. There is no wasted verbiage, and the domain/category annotation is efficiently appended. Every word earns its place, making it easy for an agent to parse quickly.

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 complexity (advanced primality test), lack of annotations, 0% schema description coverage, and no output schema, the description is insufficient. It does not cover parameter meanings, return values, behavioral traits, or usage context. For such a specialized mathematical tool, more detail is needed to ensure the agent can invoke it correctly.

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, so the description must compensate. It mentions 'n' (the number) and 'bases' implicitly but does not explain their roles, valid ranges, or interactions (e.g., that 'bases' is an array of integers used for testing). Without this semantic detail, the agent cannot understand parameter purposes beyond the schema's basic types.

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: 'Test if a number is a strong pseudoprime to given bases.' It specifies the verb ('Test'), resource ('number'), and context ('strong pseudoprime to given bases'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'miller_rabin_test' or 'solovay_strassen_test', which are also primality-related tests.

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 ('advanced_primality'), which implies context but does not specify scenarios, prerequisites, or comparisons to sibling tools such as 'aks_primality_test' or 'deterministic_miller_rabin'. Without explicit when-to-use or when-not-to-use instructions, the agent lacks practical usage direction.

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