derivative_central

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 mentions the method is 'most accurate', which adds some context about performance, but it doesn't disclose other behavioral traits such as error handling, computational complexity, or what happens with invalid inputs (e.g., non-numeric 'func' strings). For a tool with no annotations, this is a significant gap in transparency.

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 highly concise and front-loaded, consisting of a single sentence that directly states the tool's purpose and key attribute ('most accurate'). There is no wasted verbiage, and the structure efficiently conveys the essential information without unnecessary details.

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 computation tool with 3 parameters), no annotations, 0% schema description coverage, and no output schema, the description is incomplete. It lacks details on parameter meanings, behavioral traits, and expected outputs. For a tool of this nature, the description should provide more context to guide effective use, but it falls short.

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 parameters ('func', 'x', 'h') are documented in the schema. The description does not add any semantic information about these parameters—it doesn't explain what 'func' represents (e.g., a mathematical expression as a string), what 'x' is (the point at which to compute the derivative), or what 'h' is (the step size, with a default value). This fails to compensate for the lack of schema documentation.

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: 'Calculate numeric derivative using central difference method (most accurate)'. It specifies the verb ('calculate'), resource ('numeric derivative'), and method ('central difference'), and distinguishes it by noting it's 'most accurate'. However, it doesn't explicitly differentiate from its siblings 'derivative_backward' and 'derivative_forward', which are listed among the sibling tools.

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 minimal usage guidance. It mentions the method is 'most accurate', which implies a preference over other derivative methods, but it doesn't explicitly state when to use this tool versus alternatives like 'derivative_backward' or 'derivative_forward', nor does it provide context on prerequisites or exclusions. No explicit alternatives or when-not-to-use scenarios are given.

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