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Crawlora-org

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datasets_apps_search

Search resolved iOS App Store and Google Play apps using full-text queries, filters for store, price, rating, category, developer, and more. Returns clean JSON results.

Instructions

Search the apps-intelligence dataset. Searches resolved iOS App Store and Google Play apps stored in a search index. Store enum: ios, android, both. Sort enum: relevance, rating_desc, reviews_desc, installs_desc, updated_at_desc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoFull-text query over title, developer and category, max 256 characters
freeNoFilter by price; true keeps only free apps, false only paid
pageNoPage number, defaults to 1
sortNoSort enum: relevance, rating_desc, reviews_desc, installs_desc, updated_at_desc
storeNoStore enum: ios, android, both
countryNoExact storefront country filter, max 128 characters
categoryNoExact app-store category filter, max 128 characters
developerNoExact developer/publisher name filter, max 128 characters
page_sizeNoPage size, defaults to 20 and maxes at 100; page * page_size must be <= 10000
min_ratingNoMinimum store rating, 0 through 5
min_reviewsNoMinimum ratings/review count
Behavior2/5

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

No annotations are provided, so the description must carry the full behavioral burden. It only states it searches a search index and lists enums, without disclosing rate limits, data freshness, pagination behavior, or any side effects. It does not describe what is returned, leaving a significant gap.

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 extremely concise with two sentences plus an enum list. It front-loads the primary verb and resource, wasting no words. Every sentence serves a clear purpose.

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 11 parameters, no output schema, and no annotations, the description is insufficient. It does not describe the output format, common use cases, filtering behavior, or how results are structured. The agent lacks context to use the tool effectively.

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%, so each parameter is already documented in the schema. The description adds value by compactly listing the store and sort enums, but this information is also present in the schema. No additional parameter interactions or constraints are explained.

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 it searches the apps-intelligence dataset of resolved iOS and Google Play apps, and specifies store and sort enums. It distinguishes itself from sibling tools like datasets_apps_charts_search and datasets_apps_reviews_search by focusing on general app search in a curated dataset.

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

Usage Guidelines3/5

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

Usage is implied by describing the dataset as 'resolved' and listing enums, but no explicit guidance on when to use this versus alternatives like appstore_search or googleplay_search. The description does not provide exclusions or when-not scenarios.

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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