calculate_equation

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

No annotations are provided, so the description must carry the behavioral transparency burden. It only states 'Returns: {error}', which is ambiguous and does not disclose what the tool returns on success (e.g., solutions, discriminant, complex numbers) or how errors are handled.

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 concise with two sentences. The first sentence front-loads the purpose. However, the second sentence 'Returns: {error}' is somewhat cryptic and could be clearer, but overall minimal waste.

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 solves quadratic equations which can have multiple solutions, including complex ones. The description omits key details like discriminant handling, output format (even though an output schema exists), and error conditions, making it incomplete for effective use.

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 describes each parameter. The description adds context by showing the equation forms, but does not provide additional semantics like constraints or default behavior beyond what the schema offers.

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 solves 1st and 2nd degree equations, specifying the forms ax+b=0 and ax²+bx+c=0. However, it does not distinguish from the sibling tool calculate_quadratic_equation, which likely solves the same 2nd degree equations.

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?

There is no explicit guidance on when to use this tool versus alternatives like calculate_quadratic_equation. The mention to 'see list_bundles for related math calculators' is vague and does not provide clear context for tool selection.

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