Get full details for one participating restaurant by slug or name: description, all prix-fixe offers, participating weeks, Restaurant Week menu PDF, website, and OpenTable reservation link.
| Name | Required | Description | Default |
|---|---|---|---|
| restaurant | Yes | Restaurant slug (from search_restaurants) or name, e.g. 'gran-morsi' or 'Gran Morsi' |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It conveys a safe read-only operation by using 'Get' and explicitly lists the output fields, so an agent can predict what will happen. It does not mention error behavior or permissions, but for a simple read-only lookup this is a minor gap.
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 front-loads the primary action and then lists the specific return contents. Every phrase adds value, with no filler or redundancy.
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 one-parameter read-only lookup with no output schema, the description is nearly complete: it identifies the exact output fields, input format, and the singular scope. It could mention what happens when a restaurant is not found or not participating, but this is a minor omission for such a simple tool.
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 input schema already provides 100% parameter documentation, including the accepted forms (slug from search_restaurants or name) and an example. The description reinforces 'by slug or name' but does not add substantive meaning beyond the schema, so the baseline of 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?
The description uses a specific verb and resource ('Get full details for one participating restaurant') and enumerates the returned data (description, prix-fixe offers, weeks, PDF, website, OpenTable link), making its function unmistakable. It also clearly differentiates from the sibling search_restaurants tool by targeting a single restaurant rather than a search/listing.
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 clearly implies when to use the tool: when you need full details for a specific restaurant, with the input being a slug or name. It does not explicitly discuss alternatives or exclusions, but the singular 'one restaurant' and the schema hint that the slug comes from search_restaurants provide clear context.
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.