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 meaningfully reveals program-wide Saturday exclusion, variable Sunday participation, optional/combinable filters, and pagination with a total count. It does not cover rate limits, result ordering, or defaut result item fields, but the provided context is substantial and non-obvious.

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 four sentences with no wasted words. Core purpose and scope are front-loaded, followed by filter semantics, program-specific rules, and return/pagination details. Every sentence carries unique information that is not redundant with the schema.

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 no output schema and no annotations, the description covers the key program facts (date range, tiers, exclusions), pagination behavior, and the path to get_restaurant for detailed data. It could be more complete by mentioning how to discover valid filter values via list_filters or how results are ordered, but the provided context is sufficient for a competent agent to make correct initial calls.

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 high (86%), so the schema handles most parameter meaning. The description adds value beyond the schema by explaining that filters are optional and combinable, and clarifying the prix-fixe tiers ($30/$45/$60) across lunch, brunch, or dinner. This supplements parameters like price, meal, and date, making the tool's behavior easier to reason about.

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 opens with a specific verb and resource: 'Search the 396 restaurants participating in NYC Restaurant Week Summer 2026', which unambiguously defines the tool's scope. It further distinguishes itself from the sibling get_restaurant by noting that the search returns a paginated list and that get_restaurant is used for menus, reservations, and links. This clear differentiation lets an agent select the correct tool without opening other definitions.

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 states when to use this tool ('Search...'), clarifies that all filters are optional and combinable, and explicitly routes to get_restaurant for detailed restaurant info. However, it does not mention when to choose check_date or list_filters, though list_filters is referenced in the schema for valid filter values. This leaves some routing to inference 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.