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AppFactory

by gbatistuta0
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Python
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ASO Top Grossing

aso_top_grossing
Read-onlyIdempotent

List top-grossing App Store apps by storefront and category as a revenue proxy to identify proven ideas in a niche.

Instructions

List the top-grossing apps of a storefront and optional category as a revenue proxy.

Use when: hunting proven ideas in one category. To sweep all categories and storefronts use idea_harvest. An unknown genre returns ok:false, never the overall chart. Returns: {ok, apps: [...]} (untrusted_content).

Input Schema

NameRequiredDescriptionDefault
genreNoApp Store category name (e.g. 'photo_video', 'productivity', 'health') or numeric genre id; omit for all/inferred.
limitNoNumber of chart entries to return (default 25).
countryNoTwo-letter App Store storefront code, lowercase, e.g. 'us', 'gb', 'de'.us

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed3 schema fields changedv0.1.5
    • addedInput schema / properties / country / description
      Added value: +"Two-letter App Store storefront code, lowercase, e.g. 'us', 'gb', 'de'."
    • addedInput schema / properties / genre / description
      Added value: +"App Store category name (e.g. 'photo_video', 'productivity', 'health') or numeric genre id; omit for all/inferred."
    • addedInput schema / properties / limit / description
      Added value: +"Number of chart entries to return (default 25)."
  2. First observedv0.1.1

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description goes beyond that by disclosing failure semantics ('an unknown genre returns ok:false, never the overall chart') and the return envelope, which are real behavioral traits not encoded in the structured fields.

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?

Three short lines, purpose first, then routing guidance, then return/error behavior. Every sentence carries information and none is redundant.

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?

An output schema exists, so the description need not explain return values in depth, yet it still summarizes the envelope and the error path. Combined with the annotations and full schema coverage, an agent has enough to call this correctly; only minor depth (e.g. ranking or pagination semantics) is absent.

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%, so genre, limit, and country are fully documented in the schema itself. The description adds a little framing ('optional category', 'storefront') but no syntax or format detail beyond what is already there, so the baseline of 3 applies.

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 ('List the top-grossing apps') and pins down the scope ('of a storefront and optional category'), plus the purpose ('as a revenue proxy'). The sibling it is not (idea_harvest) is named, so an agent can route without opening the schema.

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

Usage Guidelines5/5

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

'Use when: hunting proven ideas in one category' gives an explicit triggering context, and the next sentence names the alternative (idea_harvest) with the condition that selects it ('sweep all categories and storefronts'). Nothing is left to inference.

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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