numerical_derivative

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the computational method but doesn't describe important behavioral aspects: what precision to expect, how step_size affects accuracy, whether the function must be continuous, what happens with invalid inputs, or what the output format will be. For a numerical computation tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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 states exactly what the tool does with zero wasted words. It's appropriately sized for a mathematical computation tool and front-loads the essential information. Every word earns its place.

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 of numerical differentiation (requires understanding of numerical stability, step size selection, function representation) with no annotations, 0% schema coverage, and no output schema, the description is insufficient. It doesn't explain how functions are represented (via 'function_type' and 'coefficients'), what numerical method variations exist, what precision to expect, or what the output looks like. For a tool with 4 parameters and mathematical complexity, this leaves too many unanswered questions.

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 0%, so all 4 parameters are undocumented in the schema. The description mentions '函数' (function) and '点' (point), which partially explains 'function_type' and 'point', but doesn't explain 'coefficients' (what format? polynomial coefficients?), 'step_size' (default value? typical range?), or the relationship between parameters. The description adds minimal value beyond what can be inferred from parameter names.

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 action ('计算函数在某点的导数' - compute derivative of function at a point) and specifies the method ('使用中心差分法' - using central difference method). It distinguishes from siblings like 'newton_method' or 'bisection_method' by focusing specifically on numerical differentiation rather than root-finding or other numerical methods. However, it doesn't explicitly differentiate from potential similar tools that might not exist in the sibling list.

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. There are no mentions of when this method is appropriate (e.g., for functions without analytical derivatives), when not to use it (e.g., for highly oscillatory functions), or what alternatives exist among the siblings. The agent must infer usage from the method name alone.

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