NYC restaurant inspection grades

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

Annotations already declare read-only, idempotent, open-world, non-destructive, so the safety profile is covered. The description adds genuinely new behavioral context beyond the annotations: results are 'most recently inspected first' and results are paginated, which the agent needs to page correctly.

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?

Three sentences, each earning its place: identity, filtering constraint, return shape and ordering. The critical 'at least one' constraint is front-loaded rather than buried.

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?

With an output schema present and full schema coverage, the description only needs to add calling context, and it does: it names the data source, the required filter condition, the sort order and the pagination model. Nothing blocking a correct call is missing.

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%, with examples and constraints for name, cuisine, zip_code, borough enum, limit and offset already documented. The description restates the filter set but adds no syntax or format detail beyond the schema, so the baseline 3 applies.

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?

States a specific verb and resource ('Look up NYC restaurant health inspections (DOHMH)') and enumerates the filter dimensions, so the agent knows exactly what it returns. It does not explicitly distinguish itself from generic dataset siblings like search_datasets or query_dataset, so sibling differentiation is left implicit.

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 parenthetical '(at least one)' establishes a real constraint on how to invoke the tool, which is useful guidance. However, it never says when to prefer this over search_datasets/query_dataset, so usage is only implied rather than routed.

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