calculate_tip_worldwide

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

No annotations are provided, so the description carries the full burden. It mentions inputs (bill, country) and outputs (recommended tip and total), but does not disclose any side effects, idempotency, or rate limits. Since it is a calculation tool, the behavior is largely implied, but transparency could be improved with explicit statements.

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 two sentences long, front-loaded with purpose and examples, and ends with a usage hint. Every sentence adds value; no redundancy or filler.

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 two parameters, full schema coverage, and likely output schema (from context), the description covers purpose, inputs, and behavior adequately. It could mention the pure computational nature, but overall it is sufficiently complete for the tool's simplicity.

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 schema covers 100% of parameters with descriptions ('Bill' and 'Country'), and the description adds cultural context (e.g., US 18-22%) that enhances understanding beyond the schema. This provides meaningful addition to the parameter semantics.

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 restaurant tips based on local customs with specific examples (US 18-22%, FR included, JP no tip). However, it does not explicitly differentiate from sibling tools like 'calculate_tip' or 'calculate_tip_split', which could cause confusion given the large number of siblings.

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 suggests using when traveling, providing a use case. However, it lacks guidance on when not to use the tool or explicit alternatives (only mentions list_bundles for related calculators, not for direct comparison). The guidance is minimal.

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