sympy_is_square

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

No annotations present, so description must disclose behavior. It does not mention return type (e.g., boolean), input constraints (e.g., integer, non-negative), or handling of invalid input. Output schema exists but is not referenced.

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 a single concise sentence, but it is too short to be adequately informative. It could be improved 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 tool's simplicity and presence of an output schema, the description just barely covers the essential purpose but omits critical details about input format and output type, making it incomplete for an agent to use confidently.

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 0%, but description adds no semantics beyond the parameter name 'n'. It fails to clarify that n should be a numeric string or what values are valid (e.g., integer only).

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 a perfect square' clearly states the tool's specific verb and resource, and immediately distinguishes it from sibling tools like sympy_isprime or sympy_perfect_power.

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?

Implied usage from the description, but no explicit when-to-use or when-not-to-use guidance. Could mention alternatives like sympy_perfect_power for generalized perfect power checks.

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