sympy_isprime

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

With no annotations, the description must fully disclose behavior but does not mention return type (likely boolean), error handling for non-integer inputs, or domain restrictions (e.g., positive integers). The agent is left uncertain about the tool's exact behavior beyond the basic check.

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 at 4 words, front-loading the core purpose without redundant information. Every word is necessary and informative for a simple tool.

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?

The tool has a simple input/output but the description lacks explicit mention of the return value (true/false). Although an output schema may exist, the description would benefit from clarifying the boolean result. Edge cases (e.g., negative numbers) are not addressed, making it minimally 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?

Schema description coverage is 100% (parameter 'n' documented as 'Integer'). The description adds no extra meaning beyond the schema; it merely restates 'n' without clarifying input format (string representation) or accepted ranges. Baseline 3 applies due to high schema 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 'Check if n is prime' uses a specific verb ('check') and clearly identifies the resource (primality of n). It distinguishes this tool from siblings like 'sympy_prime' (which generates primes) or 'sympy_factorint' (factorization), leaving no ambiguity about its function.

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 (e.g., 'sympy_factorint' for factoring, 'sympy_is_square' for squareness). There is no mention of input constraints or performance implications, leaving the agent to infer usage context.

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