complex_magnitude

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

No annotations are provided, so the description carries full burden. While '计算' (calculate) implies a read-only mathematical operation, the description doesn't disclose any behavioral traits like error conditions (e.g., handling of non-numeric inputs), performance characteristics, or what happens with special values (infinity, NaN). It's a minimal functional statement without behavioral context.

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, efficient sentence in Chinese that directly states the tool's purpose. There's zero wasted language or unnecessary elaboration. It's appropriately sized for a simple mathematical function.

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?

For a simple mathematical function with 2 parameters and no annotations, the description is minimally complete. It states what the tool does but lacks behavioral details (error handling, special cases) and usage guidance. Without an output schema, it doesn't describe the return value format. The description meets basic requirements but leaves gaps for agent understanding.

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?

With 0% schema description coverage (no parameter descriptions in schema), the description doesn't explicitly mention parameters. However, for a mathematical function with exactly 2 parameters (real and imag), the tool name and description strongly imply the parameters represent the real and imaginary parts of a complex number. This is adequate semantic context for such a standard operation, though not explicit.

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 '计算复数的模长（绝对值）' (Calculate the magnitude/absolute value of a complex number) clearly states the specific verb ('计算' - calculate) and resource ('复数的模长' - magnitude of complex number). It distinguishes from sibling tools like 'abs' (which might be for real numbers) and 'complex_argument' (which calculates the argument/phase).

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 doesn't mention when to use 'complex_magnitude' instead of 'abs' (which might handle both real and complex numbers) or other mathematical operations. No context about prerequisites or typical use cases is given.

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