divergence

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

No annotations are supplied, so the description carries the full behavioral burden and does not meet it. It says nothing about whether the computation is symbolic or numeric, what form the result takes (a scalar expression vs. an evaluated grid), or how errors are surfaced.

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?

A single front-loaded sentence with no filler or redundancy. It is efficiently written, though its brevity is partly under-specification rather than pure economy.

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 two-required-parameter math tool with no annotations and no output schema, the description omits the return type and the field/variable correspondence. An agent has no way to know the expected input shape beyond the bare array types.

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 the description must compensate, and it only loosely maps 'vector field' and 'given variables' onto the two params. It never explains that 'field' is a list of component expressions or how its ordering relates to 'variables', which is essential for a correct call.

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?

States a specific verb+resource: 'Compute the divergence of a vector field'. The operation is intrinsically distinct from siblings gradient, curl and laplacian, so an agent can identify what it does. It does not explicitly contrast itself with those siblings, keeping it short of a 5.

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 indication of when to choose this over curl, gradient or laplacian, and no prerequisites or operand-type expectations. Usage must be inferred purely from the mathematical name.

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