Crawlora MCP

datasets_apps_charts_facets

Read-only

Return up to 50 facet buckets with counts for one apps-charts field using the filters from datasets_apps_charts_search. facet accepts store, collection, chart_type, platform, category, country, app_id, snapshot_date, or free; page and page_size do not change counts; sort is validated but does not change bucket order. A snapshot_date facet without date spans all history. Other facets use the same search date scope: latest snapshot by default when applicable, full history for a single-app filter or sort=date_desc, and only the requested day when date is supplied. Use datasets_apps_charts_search to find chart 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_charts_facets"
  }
}

Input Schema

Name	Required	Description
`q`	No	Optional full-text query over chart-entry title and developer, max 256 characters.
`date`	No	Optional snapshot date filter, yyyy-MM-dd. Defaults to the latest available snapshot (today's chart).
`page`	No	Result page number, 1-based, default 1; page times page_size must not exceed 10000.
`sort`	No	Optional sort order. Allowed values: rank (chart order), rank_desc, date_desc (rank over time). Defaults to rank.
`facet`	Yes	required,Required facet. Allowed values: store, collection, chart_type, platform, category, country, app_id, snapshot_date, free.
`store`	No	Optional store filter. Allowed values: ios, android.
`app_id`	No	Optional exact app filter — the iOS numeric track id or the Android package name; pair with sort=date_desc for an app's rank history.
`country`	No	Optional storefront country filter, max 128 characters, e.g. us.
`category`	No	Optional store category/genre filter, max 128 characters. Empty for the overall (all-category) charts.
`platform`	No	Optional Apple device-platform filter, iOS charts only. Allowed values: phone, pad, mac.
`page_size`	No	Page size, default 20, max 100; page times page_size must not exceed 10000.
`chart_type`	No	Optional chart filter. Allowed values: top_free, top_paid, top_grossing, new.
`collection`	No	Optional raw store collection id filter (e.g. topgrossingapplications, GROSSING), max 128 characters.

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

Behavior5/5

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

Annotations already declare readOnlyHint and openWorldHint, yet the description still adds non-obvious behavior: a hard 50-bucket cap, page/page_size not affecting counts, sort being validated but not reordering buckets, and a precise date-scope model (snapshot_date spans all history; other facets default to latest snapshot, use full history for single-app or sort=date_desc, or the requested day when date is supplied). This is exactly the kind of context annotations cannot express.

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 core fact (counts, 50 buckets, one field) is front-loaded and every subsequent clause carries real information. The date-scope sentence is long and dense, but it is not padding; it is the single most decision-relevant nuance after the purpose.

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?

With 13 parameters, an output schema handling return shape, and readOnly/openWorld annotations already covering safety, the description supplies the remaining load-bearing context: count cap, count-invariance of paging/sort, and the date-window rules. Minor gap: it does not clarify how the non-date facet scope interacts with an explicit q or free filter, but for a facet-count tool this is close to complete.

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 baseline is 3, but the description goes beyond the schema by explaining that page/page_size are count-neutral and that sort does not change bucket order — semantics the schema does not state. It also enumerates the allowed facet values and the date-scoping rules per facet. It does not explain every parameter (e.g. how `free` or `q` interact with facets), keeping it at 4.

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?

States a specific verb and resource: returns up to 50 facet buckets with counts for one apps-charts field. It immediately scopes the tool to aggregation over a single field and names the sibling (datasets_apps_charts_search) that the filters come from, so the agent can tell it apart from the search and item siblings.

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 closing sentence routes the agent to datasets_apps_charts_search 'to find chart records', implying this tool is for bucket counts rather than record lists. That is a clear directional signal, but it stops short of an explicit when-not/exclusion statement (e.g. 'do not use this to list charts').

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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