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get_restaurant

Read-only

Get the full picture for one restaurant in one call: Infatuation rating (0–10), price tier, address, reservation link, booking intel, review summary, tags and every guide it appears in. Use when the user names a specific restaurant. Full review prose is opt-in via include_prose (default: headline and summary only). review.headline is the source's actual headline when one exists, otherwise null — use review.summary for the descriptive text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYesCanonical restaurant id, or a name to resolve
cityYesCity slug, always required. Currently 'new-york', covering the five boroughs plus the immediate metro (within 30 km of Manhattan).
include_proseNoInclude the full review text (default false: headline + summary only)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / properties / city / description
      Previous value: -"City slug, always required. Currently 'new-york'."New value: +"City slug, always required. Currently 'new-york', covering the five boroughs plus the immediate metro (within 30 km of Manhattan)."
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

The readOnlyHint annotation already covers the safety profile. The description adds useful behavioral details beyond that: default output is headline plus summary, full prose is opt-in via include_prose, and review.headline may be null with review.summary as the descriptive fallback. This meaningfully clarifies output semantics.

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

Conciseness5/5

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

The description is dense and front-loaded: purpose, returned content, usage trigger, then parameter nuance. Every sentence contributes meaningful information without repetition or filler.

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-only single-restaurant lookup with no output schema, the description supplies a strong inventory of returned fields and clarifies the headline/summary behavior. The phrase 'booking intel' is slightly vague, and there is no mention of error cases, but overall the definition is sufficiently complete for an agent to call it correctly.

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

Parameters4/5

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

Schema description coverage is 100%, so the baseline is 3. The description adds extra value by explaining the default behavior of include_prose and what it toggles. The id and city parameters are already well documented in the schema, so the description does not need to repeat them.

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?

The description opens with a clear verb and resource: 'Get the full picture for one restaurant in one call.' It enumerates exactly what is returned, and the line 'Use when the user names a specific restaurant' distinguishes it from sibling tools like search_restaurants.

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?

The description explicitly states when to use the tool: 'Use when the user names a specific restaurant.' It provides clear context but does not explicitly name alternatives or state when not to use it, so it stops just short of full routing guidance.

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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