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opentable-mcp

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opentable_get_restaurant

Read-only

Retrieve complete OpenTable restaurant details - cuisine, price, hours, phone, amenities, ratings, availability - using ID, slug, or search URL.

Instructions

Get full details for a single OpenTable restaurant: cuisine, price band, description, address, hours, phone, payment options, features, rating/review count, and availability_token (used internally when booking). Accepts the numeric restaurant_id, a slug, a path, or the full URL from opentable_search_restaurants — passing the search result's "url" verbatim always resolves, including legacy venues served at /{slug} instead of /r/{slug}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
viewNoResponse shape: "compact" (default) drops fields the response already carries elsewhere; "full" returns every field this server understands. compact strips image/avatar URLs from the response; "full" returns OpenTable's payload untouched. No field projection: this server has no verified record of which OpenTable fields matter, and inventing one would risk dropping a field a caller needs.
restaurant_idYesNumeric restaurant_id (as returned by opentable_list_reservations / opentable_list_favorites), slug ("state-of-confusion-charlotte"), path, or full URL from opentable_search_restaurants. Passing the search result's "url" verbatim resolves both /r/{slug} and legacy /{slug} venues; a numeric id resolves via /restaurant/profile/{id}.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.19.2
    • addedInput schema / properties / view
      Added value: +{
+  "description": "Response shape: \"compact\" (default) drops fields the response already carries elsewhere; \"full\" returns every field this server understands. compact strips image/avatar URLs from the response; \"full\" returns OpenTable's payload untouched. No field projection: this server has no verified record of which OpenTable fields matter, and inventing one would risk dropping a field a caller needs.",
+  "enum": [
+    "compact",
+    "full"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changedv0.18.1
    • changedInput schema / properties / restaurant_id / description
      Previous value: -"Slug (\"state-of-confusion-charlotte\"), path, or full URL from opentable_search_restaurants. Prefer passing the search result's \"url\" verbatim — it resolves both /r/{slug} and legacy /{slug} venues. Numeric ids are not supported (they 404); use the slug/url instead."New value: +"Numeric restaurant_id (as returned by opentable_list_reservations / opentable_list_favorites), slug (\"state-of-confusion-charlotte\"), path, or full URL from opentable_search_restaurants. Passing the search result's \"url\" verbatim resolves both /r/{slug} and legacy /{slug} venues; a numeric id resolves via /restaurant/profile/{id}."
  3. First observedv0.14.3

TDQS

A4.1/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, which the description matches (a 'Get' operation). Beyond that, the description discloses genuinely useful behavior: the availability_token is for internal booking use, and the tool resolves both /r/{slug} and legacy /{slug} URLs. No contradiction with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, purpose and field list front-loaded, with input-format details in the second sentence. Dense but not bloated; the long field enumeration earns its place given there is no output schema to describe the return shape.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read tool with no output schema, the description compensates by listing the returned fields and clarifying the union-typed identifier parameter's resolution behavior. The view parameter's semantics are fully handled by the schema. Little an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and both parameters (view, restaurant_id) are already documented in detail within the schema, including the enum and the anyOf union. The description adds only a marginal guarantee ('passing the search result's url verbatim always resolves'), which is not in the schema but is a small increment over a solid baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource ('Get full details for a single OpenTable restaurant') followed by an explicit field list (cuisine, price band, address, hours, phone, etc.) that distinguishes it from siblings like opentable_search_restaurants (search) and opentable_get_profile (user profile). The availability_token mention adds precision about what is returned.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Clearly identifies where valid inputs come from (opentable_search_restaurants, opentable_list_reservations, opentable_list_favorites) and gives practical guidance on passing the search result's 'url' verbatim to guarantee resolution. It does not explicitly contrast against alternatives ('use this when... not that when...'), but the input-source guidance is strong enough to route an agent correctly.

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

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