vector_calculus_operation

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

Annotations are absent, so the description must fully convey behavioral traits. It only names the operations and does not disclose any side effects, input requirements (e.g., whether variables must be specified), or output expectations. The schema provides some detail, but the description itself adds no behavioral context beyond the operation names.

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 concise sentence that front-loads the core operations. There is no irrelevant information, and every word earns its place. It is appropriately minimal for the tool's straightforward 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?

The tool has an output schema and comprehensive parameter descriptions, so the description need not explain return values or parameter details. However, the description lacks usage guidance and behavioral context, making it only minimally complete. It is adequate for identifying the tool but leaves gaps for an agent deciding when to invoke it.

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 input schema already covers all parameters with clear descriptions (operation enumerates the four operations, expression explains scalar vs vector input, variables explains format). Since schema coverage is 100%, the description adds no extra parameter semantics. The baseline of 3 applies as the schema handles 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 clearly states the tool computes vector calculus operations (gradient, divergence, curl, laplacian), which is specific and distinguishes it from sibling tools like differentiate_expression or integrate_expression that handle scalar calculus. The resource and operations are explicit, making the purpose immediately clear.

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 provided on when to use this tool versus alternatives. It does not mention which operation to select for different input types (e.g., scalar vs vector fields), nor does it compare to related tools like differentiate_expression. The description only lists the operations without any usage context.

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