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JavaScript
Remote

datasets_apps_reviews_search

Search user app reviews from iOS App Store and Google Play using full-text queries, filters for store, app, country, and star rating, and sort by recency, score, or helpfulness.

Instructions

Search the app-reviews dataset. Searches user reviews scraped from the iOS App Store and Google Play, stored in a search index (one document per review). Store enum: ios, android. Sort enum: recent, score_desc, score_asc, helpful_desc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over review text, title and author, max 256 characters
pageNoPage number, defaults to 1
sortNoSort enum: recent, score_desc, score_asc, helpful_desc
storeNoStore enum: ios, android
app_idNoExact app filter — iOS numeric track id or Android package, max 128 characters
countryNoExact storefront country filter, max 128 characters
min_scoreNoMinimum star rating, 1 through 5
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.17.5
    • addedInput schema / properties / sort / enum
      Added value: +[
+  "recent",
+  "score_desc",
+  "score_asc",
+  "helpful_desc"
+]
    • addedInput schema / properties / store / enum
      Added value: +[
+  "ios",
+  "android"
+]
  2. Addedv1.2.0

TDQS

B3.1/5.0
Behavior2/5

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 does mention that the data is 'scraped' and stored in a 'search index', which gives some context about data provenance, but it does not state the tient is read-only, disclose rate limits, pagination limits beyond the schema, or specify what the response contains. Since there is no output schema, the agent is left without information about return structure or potential limitations.

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?

The description is concise at two sentences, front-loading the core purpose in the first sentence. However, the detailed enumeration of store and sort options is redundant with the schema and slightly lengthens the description. Overall, it is tightly written and easy to parse, though it could be slightly leaner by omitting the enum lists.

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

Completeness2/5

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

Given the tool has 8 parameters, no output schema, and no annotations, the description is insufficiently complete. It does not describe the return format (e.g., fields of a review result), pagination experience, or any usage context such as typical use cases for this dataset versus live store reviews. The description covers the 'what' but not the 'how' or 'what to expect', making it incomplete for an agent to call it correctly without extra schema digging.

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 description coverage is 100%, with every parameter having a description. The description adds the enum values for 'store' and 'sort' explicitly, but these are already present in the schema. Thus, the description adds no meaningful semantics beyond the schema, aligning with the baseline score of 3 for high schema coverage. There is no additional context about parameter interactions or usage norms.

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 clearly states the action ('Search'), the resource ('the app-reviews dataset'), and specifies the content (user reviews scraped from iOS App Store and Google Play). This distinguishes it from sibling tools like datasets_apps_search, which likely target app metadata rather than reviews. The mention of 'one document per review' adds precision about the data model, making the purpose unambiguous.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives such as googleplay_reviews or appstore_reviews, nor does it state any exclusions or prerequisites. It only describes what the tool does, leaving the agent to infer usage context from the name and sibling list. No explicit when/when-not instructions are present.

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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