calculate_curl

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 explains the tool's function and includes an example with expected output, but lacks details on error handling, performance characteristics, or side effects (e.g., whether it modifies state). This is adequate but has gaps for a tool with no annotation coverage.

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 well-structured with a purpose statement, parameter explanation, example, and return value note. It is appropriately sized and front-loaded, though the example could be slightly more concise. Every sentence adds value, with no redundant information.

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 tool's moderate complexity (mathematical operation with dependencies), no annotations, and no output schema, the description is reasonably complete. It covers purpose, parameter semantics, usage example, and return value. However, it could improve by addressing error cases or linking more explicitly to sibling tools for context.

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 has 0% description coverage, but the description compensates by explaining the parameter's meaning ('The key of the vector field expression') and showing its usage in the example. It clarifies that the parameter references a previously created vector field, adding significant value beyond the schema's basic type information.

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: 'Calculates the curl of a vector field using SymPy's curl function.' It specifies the verb ('calculates'), resource ('curl of a vector field'), and implementation method ('using SymPy's curl function'), distinguishing it from siblings like calculate_divergence or calculate_gradient.

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 clear context through an example showing prerequisite steps (creating a coordinate system and vector field) and the tool's role in a workflow. However, it does not explicitly state when to use this tool versus alternatives like calculate_divergence or when not to use it, which prevents a perfect score.

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