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place_reviews

Get user reviews for a Google Maps place by place_id. Each review includes rating, review text, author info, timestamps, photos, and any owner response. Each page returns up to 10 reviews. The language parameter filters reviews to those originally written in that language (language=fr returns French reviews only, language=es returns Spanish only, etc.). Combine with sort_by (4 options) to surface a much larger pool.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pagesNoNumber of pages, 1-10 (default: 1). Each page returns up to 10 reviews and is billed as one request.
countryNo2-letter country code (default: "us")us
sort_byNoSort ordermost_relevant
languageNo2-letter language code (default: "en"). Filters reviews to those originally written in this language.en
place_idYesGoogle place_id
get_sentimentNoAdd AI sentiment analysis (Plutchik emotions, dominant_emotion, intensity, and positive/negative/neutral polarity) to each result. Adds a small per-page surcharge.
translate_reviewsNoTranslate the returned reviews into the requested language

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses page size limits (up to 10 reviews per page), the language filtering semantics (originally written language), and the interaction between sort_by and pagination. This goes beyond the schema and gives users meaningful behavioral expectations.

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 three concise sentences, front-loaded with the primary action and place_id requirement. It packs essential details (review contents, pagination, language filtering, sort behavior) without redundancy or fluff.

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?

Given no output schema and no annotations, the description adequately covers what reviews contain, pagination limits, and parameter behaviors. It does not mention sentiment analysis or translation, but the schema descriptions for those parameters suffice. The description is complete enough for a focused, read-only tool.

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 coverage is 100%, so baseline is 3. The description adds value by clarifying the language parameter with concrete examples (fr returns French reviews only) and explaining the strategic use of sort_by to surface more reviews. This enriches parameter understanding beyond the schema descriptions.

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 uses a specific verb ('Get') and resource ('user reviews for a Google Maps place by place_id'), clearly distinguishing it from sibling tools like place_details or place_photos. It immediately establishes the tool's core function and scope.

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 provides clear context for use (fetching Google Maps reviews by place_id) and gives practical guidance on combining sort_by with multiple pages to surface a larger review pool. It does not explicitly contrast with sibling tools, but this is fairly obvious given the focused purpose.

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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TDQS

B3.1/5.0
Disambiguation4/5

Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.

Naming Consistency4/5

The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.

Tool Count2/5

74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.

Completeness4/5

The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.

Resources