gradient_vector

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only says 'compute the gradient vector' but does not clarify whether the result is symbolic, evaluated at a point, returned as a list, or how the optional point interacts with symbolic computation.

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 clear sentence that front-loads the operation with no filler. It is appropriately minimal for a straightforward mathematical tool.

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?

The core operation is clear and the schema covers all parameters, but there is no output schema and no annotation context. A note on return format or a distinguishing remark relative to sibling differentiation tools would make the description more complete for correct tool selection and invocation.

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 100%, so the input schema already documents all three parameters. The description adds no additional parameter-level meaning, but the baseline of 3 is appropriate since the schema does the heavy lifting.

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 states a specific verb ('Compute') and resource ('gradient vector of a multivariable function'), making the core purpose clear. It does not explicitly distinguish itself from 'partial_derivatives', but the term 'gradient vector' is mathematically standard and self-identifying enough.

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?

No guidance is given on when to use this tool versus sibling tools like 'partial_derivatives', 'differentiate_symbolic', or 'chain_rule'. The agent must infer usage from the name and schema alone, with no stated exclusions or alternative conditions.

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