Crawlora MCP

datasets_apps_facets

Read-only

Return up to 50 facet buckets with counts for one apps field using the filters from datasets_apps_search. facet accepts store, platforms, category, country, developer, or free; page and page_size do not change counts; sort is validated but does not change bucket order. Use datasets_apps_search to find app records.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "facet": "<facet>"
    },
    "name": "datasets_apps_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Optional full-text query over app title, developer and category, max 256 characters.
`free`	No	Optional price filter; true keeps only free apps, false only paid.
`page`	No	Result page number, 1-based, default 1; page times page_size must not exceed 10000.
`sort`	No	Optional sort order. Allowed values: relevance, rating_desc, reviews_desc, installs_desc, updated_at_desc, popularity_desc. popularity_desc ranks by Popularity (ratings count times average stars). Defaults to relevance with q, otherwise reviews_desc.
`facet`	Yes	required,Required facet. Allowed values: store, platforms, category, country, developer, free.
`store`	No	Optional store filter. Allowed values: ios, android, both.
`country`	No	Optional exact storefront country filter, max 128 characters, e.g. us.
`category`	No	Optional exact app-store category filter, max 128 characters, e.g. Productivity.
`developer`	No	Optional exact developer/publisher name filter, max 128 characters.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.
`platforms`	No	Optional repeatable device-platform filter; matches apps confirmed on AT LEAST ONE (OR) of the given values, e.g. platforms=mac&platforms=tv. Allowed values: phone, pad, tablet, mac, tv, chromebook, watch, vision, xr, car, windows. Platform coverage is still being backfilled so omitting this returns the full corpus regardless of platform.
`min_rating`	No	Optional minimum store rating, from 0 through 5.
`min_reviews`	No	Optional minimum ratings/review count, must be 0 or greater.

Output Schema

Name	Required	Description	Default
`data`	Yes	The tool result payload (shape varies per tool; see each tool's docs resource).

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 2, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds useful behavior beyond annotations: the 50-bucket cap, that page/page_size do not affect counts, and that sort is validated but does not affect bucket order.

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 primary action and result shape, then adds only high-value caveats about facet values, pagination, and sort. Every sentence contributes useful information.

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?

With an output schema present and annotations covering the safety profile, the description supplies what remains necessary: facet choices, filter inheritance from datasets_apps_search, bucket cap, and non-effects of pagination and sort.

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 the schema carries the parameter documentation baseline. The description adds semantics specifically for page, page_size, and sort by explaining that they do not change facet counts or bucket order.

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 and resource: 'Return up to 50 facet buckets with counts for one apps field.' It enumerates the valid facet values and explicitly distinguishes the tool from datasets_apps_search by saying the latter is for app records.

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 clearly states that facet counts reuse the filters from datasets_apps_search and directs record lookup to datasets_apps_search instead. It does not give explicit when-not guidance, but the intended use is unambiguous.

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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