differentiate_symbolic

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. It states only the computation action and library, but does not mention the output format (e.g., SymPy expression string), error handling for invalid expressions, or any limitations. With no annotations, this is a significant transparency gap.

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, front-loaded sentence that names the action, resource, and library with zero wasted words. It is appropriately sized for a straightforward computational 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?

For a tool with four well-documented parameters and no output schema, the description covers the core purpose but leaves out expected return value format and any behavioral caveats. The completeness is adequate for basic invocation but not rich enough to guide an agent through edge cases or result interpretation.

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 schema already documents all parameters with defaults and examples. The description adds no additional parameter-level insight beyond the generic term 'mathematical expressions.' Baseline 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 uses a specific verb ('Compute') and resource ('symbolic derivatives of mathematical expressions'), naming the underlying library (SymPy). The word 'symbolic' directly distinguishes it from the sibling tool differentiate_numerical, and the single-variable focus is implied by the 'variable' parameter.

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 explicit when-to-use or alternative routing is provided. However, the term 'symbolic' strongly implies use when an exact derivative is needed, contrasting with the numerical sibling. This is an implied usage rather than explicit guidance.

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