Check whether a date (YYYY-MM-DD) falls within NYC Restaurant Week Summer 2026 and what the rules are that day (which program week, Saturday exclusion, Sunday limitations), plus how many restaurants participate that day.
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | Date to check, YYYY-MM-DD |
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 discloses the behavioral logic: returns program week, Saturday exclusion, Sunday limitations, and participation count. It does not explicitly state side-effect-free or error behavior, but the 'check' nature makes it clear it is a read-only operation.
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, well-structured sentence that efficiently states the action, input format, and output details without any fluff. Every part is informative and necessary.
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 simple one-parameter tool with no annotations or output schema, the description adequately explains the check logic and return content (rules, program week, exclusions, participation count). It could mention edge cases like invalid dates, but the schema pattern already restricts input, making it sufficiently complete.
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 the only parameter 'date' with a description ('Date to check, YYYY-MM-DD') and pattern. The description restates the format and adds context about its role in evaluating Restaurant Week, but this adds minimal value beyond the schema's existing description. Baseline of 3 is appropriate.
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's purpose: checking whether a date falls within NYC Restaurant Week Summer 2026 and returning day-specific rules and participation count. This is a specific verb+resource combination that is distinct from sibling tools like get_restaurant, list_filters, and search_restaurants.
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 implies when to use the tool: when you have a date and need to know if it falls within Restaurant Week and what rules apply. It provides clear context but does not explicitly mention alternatives or exclusions, though siblings are obviously different in scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a clearly distinct purpose: date validation, single restaurant details, filter enumeration, and restaurant search. There is no overlap between them; an agent can easily select the right tool for the task.
All tool names follow a consistent verb_noun pattern in snake_case (check_date, get_restaurant, list_filters, search_restaurants). This uniform convention makes the API predictable and easy to reason about.
With 4 tools, the server is well-scoped for a restaurant week program: search, detail lookup, date rules, and filter options. Each tool is essential and the count is appropriate for the domain.
The tool surface covers the primary workflows: searching restaurants, retrieving individual details, checking date-specific rules, and exploring available filters. No meaningful gaps exist; reservation and menu links are provided within restaurant details.