complex_multiply

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. The description only states what the tool does without any additional context about its behavior, such as error handling, performance characteristics, or output format. This leaves significant gaps in understanding how the tool operates 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 with a single sentence: '计算两个复数的乘积'. It is front-loaded and wastes no words, making it easy to parse quickly. Every part of the sentence directly contributes to understanding the tool's purpose.

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 complexity (a mathematical operation with four parameters), lack of annotations, and no output schema, the description is incomplete. It doesn't explain the input parameters, expected output format, or any behavioral traits. For a tool with this level of detail required, the description falls short in providing enough context for effective use.

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 schema description coverage is 0%, meaning none of the four parameters (a_real, a_imag, b_real, b_imag) are documented in the schema. The description does not compensate by explaining what these parameters represent (e.g., real and imaginary parts of two complex numbers). This lack of semantic information makes it difficult for an agent to understand how to invoke the tool correctly.

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: '计算两个复数的乘积' (calculates the product of two complex numbers). It specifies the verb '计算' (calculate) and resource '两个复数的乘积' (product of two complex numbers), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like complex_add, complex_divide, or complex_subtract, which is why it doesn't achieve 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 doesn't mention any prerequisites, context for use, or comparisons to sibling tools such as complex_add or complex_divide. Without this information, the agent lacks direction on selecting this tool appropriately among similar options.

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