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by Crawlora-org
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JavaScript
Remote

grubhub_restaurant_reviews

Fetch a single Grubhub restaurant's customer reviews and 1-5 star rating histogram, including star ratings, review text, reviewer details, dates, sentiment, diner type, and ordered menu items.

Instructions

Get one Grubhub restaurant's reviews and rating histogram. Returns one restaurant's customer reviews plus its 1-5 star rating histogram. Each review carries the star rating, the written body, the reviewer's display name and how many reviews they have written, the review date, Grubhub's own sentiment classification, the diner type, and the specific menu items the review is attached to -- Grubhub ties each review to what the diner actually ordered. sort accepts timeCreated_desc (most recent, the default) and ratingValue_desc (highest rated). Note the written-review corpus and the headline rating shown in search are separate systems upstream, so a restaurant can report a large rating count in search while returning few or no written reviews here.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo1-based page number (default 1)
sortNoOne of timeCreated_desc, ratingValue_desc. Default timeCreated_desc.
page_sizeNoReviews per page, 1-50 (default 20)
restaurant_idYesGrubhub's numeric restaurant id, from /grubhub/search

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "timeCreated_desc",
+  "ratingValue_desc"
+]
  2. Addedv1.16.2

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries full burden. It discloses the detailed review fields, the sort options and defaults, and a notable caveat that the written-review corpus is separate from the search headline rating, so counts may not match. This is valuable behavioral context beyond a simple fetch.

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 front-loads the core purpose in the first sentence, then adds per-review field details, sort behavior, and the upstream-systems caveat. Every sentence adds distinct value with no fluff.

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

Completeness5/5

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

There is no output schema, so the description compensates by enumerating exactly what each review contains and mentioning the histogram. It also covers sort behavior, pagination via schema, and the data-consistency caveat, making it a complete picture for an agent.

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%, with all four parameters already described in the schema. The description repeats the sort values and default but doesn't add new meaning beyond the schema, so it meets the 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?

The description opens with a specific verb and resource: 'Get one Grubhub restaurant's reviews and rating histogram.' It clearly differentiates from sibling tools like grubhub_restaurant or grubhub_restaurant_menu by focusing on reviews and histogram for a single restaurant.

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?

It's clear this tool is for fetching reviews for a specific Grubhub restaurant, and the description notes the input restaurant_id comes from /grubhub/search, implying a prerequisite workflow. However, it doesn't explicitly state when to prefer this over alternative review tools or mention exclusions, so it stops short of full 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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