mcp-sympy

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 checks for perfect squares but does not reveal critical traits such as input constraints (e.g., that 'n' is a string representing a number), performance considerations, error handling, or the return format. This leaves significant gaps in understanding how the tool behaves beyond its basic function.

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 with a single sentence: 'Check if n is a perfect square.' It wastes no words and directly communicates the core function, making it easy to parse and understand 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 low complexity (one parameter, simple boolean check) and the presence of an output schema (which likely defines the return value), the description is minimally complete. However, it lacks details on input constraints and behavioral traits, which are important for proper usage. The output schema helps, but the description could be more informative to fully guide an AI agent.

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, with one parameter 'n' of type string. The description adds minimal semantics by implying 'n' is a number to check, but it does not specify format (e.g., integer, decimal), range, or examples. Given the low schema coverage, the description fails to compensate adequately, leaving the parameter poorly documented.

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: 'Check if n is a perfect square.' It uses a specific verb ('Check') and identifies the resource ('n'), making the function unambiguous. However, it does not explicitly differentiate from sibling tools like 'sympy_isprime' or 'sympy_perfect_power', which serve different but related mathematical checks, so it falls short of a perfect score.

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 lacks context such as prerequisites (e.g., input type), exclusions, or comparisons to siblings like 'sympy_isprime' for prime number checks or 'sympy_perfect_power' for other perfect powers. This absence of usage instructions limits its effectiveness for an AI agent.

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